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    	<div class="vlar-pap-cont-item-t">Research Themes</div>
        <div class="vlar-pap-cont-item-c">
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        	<div class="vlar-pap-cont-item-c-si vlar-import-content">
                <ul>
                    <li><strong>ML</strong>: unsupervised learning, disentangled representation learning, zero-shot learning, etc.</li>
                    <li><strong>CV</strong>: 3D reconstruction, 3D semantic/instance segmentation, neural rendering, etc.</li>
                    <li><strong>Robotics</strong>: interaction with 3D scenes, autonomous navigation, path planning, etc.</li>
                </ul>
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	<div class="vlar-pap-cont-item">
    	<div class="vlar-pap-cont-item-t">Research Papers</div>
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         <!--单条paper记录 begin-->
        	<div class="vlar-pap-cont-item-c-si vlar-separatorLine">
            	<div class="vlar-pap-cont-item-c-si-img"><img src="vLAR/papers/24_icml_osn.gif"  ></div>
                <div class="vlar-pap-cont-item-c-si-c">
                        <p><a href="https://arxiv.org/abs/2407.05615">
                        <div class="papertitle">OSN: Infinite Representations of Dynamic 3D Scenes from Monocular Videos</div></a>
                        Z. Song, J. Li, <strong>B. Yang</strong> <br>
                        <em>International Conference on Machine Learning (ICML)</em>, 2024
                        <br>
                        <a href="https://arxiv.org/abs/2407.05615">arXiv</a> /
                        <a href="https://github.com/vLAR-group/OSN"><font color="red">Code</font></a>
                        <iframe src="https://ghbtns.com/github-btn.html?user=vLAR-group&repo=OSN&type=star&count=true&size=small" frameborder="0" scrolling="0" width="120px" height="20px"></iframe>
                        <p></p>
                        <p align="justify" style="font-size:13px">We present the first framework to represent dynamic 3D scenes in infinitely many ways from a monocular RGB video.</p>
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        <!--单条paper记录 begin-->
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            	<div class="vlar-pap-cont-item-c-si-img"><img src="vLAR/papers/22_neurips_ogc.gif" ></div>
                <div class="vlar-pap-cont-item-c-si-c">
                        <p><a href="https://ieeexplore.ieee.org/abstract/document/10551495">
                        <div class="papertitle">Unsupervised 3D Object Segmentation of Point Clouds by Geometry Consistency</div></a>
                        Z. Song, <strong>B. Yang</strong><br>
                        <em>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)</em>, 2024 <font color="red"><strong>(IF=20.8)</strong></font><br>
                        <a href="https://ieeexplore.ieee.org/abstract/document/10551495">IEEE Xplore</a> /
                        <a href="https://github.com/vLAR-group/OGC"><font color="red">Code</font></a>
                        <iframe src="https://ghbtns.com/github-btn.html?user=vLAR-group&repo=OGC&type=star&count=true&size=small"
                        frameborder="0" scrolling="0" width="100px" height="20px"></iframe>
                        <p align="justify" style="font-size:13px">The journal version of our OGC at NeurIPS 2022. More experiments and analysis are included.</p>

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                <div class="vlar-clear"></div>
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        <!--单条paper记录 begin-->
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            	<div class="vlar-pap-cont-item-c-si-img"><img src="vLAR/papers/24_icra_dyncatch.gif"  ></div>
                <div class="vlar-pap-cont-item-c-si-c">
                        <p><a href="https://ieeexplore.ieee.org/document/10611106">
                        <div class="papertitle">Learning to Catch Reactive Objects with a Behavior Predictor</div></a>
                        K. Lu, JX. Zhong, <strong>B. Yang</strong>, B. Wang, A. Markham <br>
                        <em>IEEE International Conference on Robotics and Automation (ICRA)</em>, 2024
                        <br>
                        <a href="https://kl-research.github.io/dyncatch">Project Page</a>
                        <p></p>
                        <p align="justify" style="font-size:13px">We present a novel framework to track and catch reactive objects in a dynamic 3D world.</p>
                </div>
                <div class="vlar-clear"></div>
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        <!--单条paper记录 begin-->
        	<div class="vlar-pap-cont-item-c-si vlar-separatorLine">
            	<div class="vlar-pap-cont-item-c-si-img"><img src="vLAR/papers/24_ijcv_unobjseg.png"  ></div>
                <div class="vlar-pap-cont-item-c-si-c">
                        <p><a href="https://arxiv.org/abs/2312.04947">
                        <div class="papertitle">Benchmarking and Analysis of Unsupervised Object Segmentation from Real-World Single Images</div></a>
                        Y. Yang, <strong>B. Yang</strong> <br>
                        <em>International Journal of Computer Vision (IJCV)</em>, 2024 <font color="red"><strong>(IF=11.6)</strong></font><br>
                        <a href="https://arxiv.org/abs/2312.04947">arXiv</a> /
                        <a href="https://link.springer.com/article/10.1007/s11263-023-01973-w">Springer Access</a> /
                        <a href="https://github.com/vLAR-group/UnsupObjSeg"><font color="red">Code</font></a>
                        <iframe src="https://ghbtns.com/github-btn.html?user=vLAR-group&repo=UnsupObjSeg&type=star&count=true&size=small"
                        frameborder="0" scrolling="0" width="100px" height="20px"></iframe>
                        <p></p>
                        <p align="justify" style="font-size:13px">The journal version of our paper at NeurIPS 2022. Complete benchmark and analysis are included.</p>
                </div>
                <div class="vlar-clear"></div>
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        <!--单条paper记录 begin-->
        	<div class="vlar-pap-cont-item-c-si vlar-separatorLine">
            	<div class="vlar-pap-cont-item-c-si-img"><img src="vLAR/papers/23_neurips_nvfi.gif"  ></div>
                <div class="vlar-pap-cont-item-c-si-c">
                        <p><a href="https://arxiv.org/abs/2312.06398">
                        <div class="papertitle">NVFi: Neural Velocity Fields for 3D Physics Learning from Dynamic Videos</div></a>
                        J. Li, Z. Song, <strong>B. Yang</strong> <br>
                        <em>Advances in Neural Information Processing Systems (NeurIPS)</em>, 2023
                        <br>
                        <a href="https://arxiv.org/abs/2312.06398">arXiv</a> /
                        <a href="https://github.com/vLAR-group/NVFi"><font color="red">Code</font></a>
                        <iframe src="https://ghbtns.com/github-btn.html?user=vLAR-group&repo=NVFi&type=star&count=true&size=small" frameborder="0" scrolling="0" width="120px" height="20px"></iframe>
                        <p></p>
                        <p align="justify" style="font-size:13px">We present a novel framework to simultaneously learn the geometry, appearance, and physical velocity of 3D scenes.</p>
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            	<div class="vlar-pap-cont-item-c-si-img"><img src="vLAR/papers/23_neurips_raydf.gif"  ></div>
                <div class="vlar-pap-cont-item-c-si-c">
                        <p><a href="https://arxiv.org/abs/2310.19629">
                        <div class="papertitle">RayDF: Neural Ray-surface Distance Fields with Multi-view Consistency</div></a>
                        Z. Liu, <strong>B. Yang*</strong>, Y. Luximon, A. Kumar, J. Li<br>
                        <em>Advances in Neural Information Processing Systems (NeurIPS)</em>, 2023
                        <br>
                        <a href="https://arxiv.org/abs/2310.19629">arXiv</a> /
                    	<a href="https://vlar-group.github.io/RayDF.html">Project Page</a> /
                        <a href="https://github.com/vLAR-group/RayDF"><font color="red">Code</font></a>
                        <iframe src="https://ghbtns.com/github-btn.html?user=vLAR-group&repo=RayDF&type=star&count=true&size=small" frameborder="0" scrolling="0" width="120px" height="20px"></iframe>
                        <br>(* indicates corresponding author)
                        <p align="justify" style="font-size:13px">We propose a novel ray-based 3D shape representation, achieving a 1000x faster speed in rendering.</p>
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                <div class="vlar-clear"></div>
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        	<div class="vlar-pap-cont-item-c-si vlar-separatorLine">
            	<div class="vlar-pap-cont-item-c-si-img"><img src="vLAR/papers/23_cvpr_growsp.gif"  ></div>
                <div class="vlar-pap-cont-item-c-si-c">
                        <p><a href="https://arxiv.org/abs/2305.16404">
                        <div class="papertitle">GrowSP: Unsupervised Semantic Segmentation of 3D Point Clouds</div></a>
                        Z. Zhang, <strong>B. Yang*</strong>, B. Wang, B. Li<br>
                        <em>IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</em>, 2023
                        <br>
                        <a href="https://arxiv.org/abs/2305.16404">arXiv</a> /
                        <a href="https://github.com/vLAR-group/GrowSP"><font color="red">Code</font></a>
                        <iframe src="https://ghbtns.com/github-btn.html?user=vLAR-group&repo=GrowSP&type=star&count=true&size=small" frameborder="0" scrolling="0" width="120px" height="20px"></iframe>
                        <br>(* indicates corresponding author)
                        <p align="justify" style="font-size:13px">We propose the first unsupervised 3D semantic segmentation method, learning from growing superpoints in point clouds.</p>
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                <div class="vlar-clear"></div>
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        <!--单条paper记录 begin-->
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            	<div class="vlar-pap-cont-item-c-si-img"><img src="vLAR/papers/23_iclr_dmnerf.gif"  ></div>
                <div class="vlar-pap-cont-item-c-si-c">
                        <p><a href="https://arxiv.org/abs/2208.07227">
                        <div class="papertitle">DM-NeRF: 3D Scene Geometry Decomposition and Manipulation from 2D Images</div></a>
                        B. Wang, L. Chen, <strong>B. Yang*</strong><br>
                        <em>International Conference on Learning Representations (ICLR)</em>, 2023
                        <br>
                        <a href="https://arxiv.org/abs/2208.07227">arXiv</a> /
                        <a href="https://twitter.com/vLAR_Group/status/1564216685640695808?s=20&t=h9Bd66jzr-mDM8eSXbMelg">Tweet</a> /
                        <a href="https://github.com/vLAR-group/DM-NeRF"><font color="red">Code</font></a>
                        <iframe src="https://ghbtns.com/github-btn.html?user=vLAR-group&repo=DM-NeRF&type=star&count=true&size=small" frameborder="0" scrolling="0" width="120px" height="20px"></iframe>
                        <br>(* indicates corresponding author)
                        <p align="justify" style="font-size:13px">We introduce a single pipeline to simultaneously reconstruct, decompose, manipulate and render complex 3D scenes.</p>
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                <div class="vlar-clear"></div>
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        	<div class="vlar-pap-cont-item-c-si vlar-separatorLine">
            	<div class="vlar-pap-cont-item-c-si-img"><img src="vLAR/papers/23_icra_decouple_maniskill.gif"  ></div>
                <div class="vlar-pap-cont-item-c-si-c">
                    <p><a href="https://deerkk.github.io/DecoupleManiSkill">
                    <div class="papertitle">Decoupling Skill Learning from Robotic Control for Generalizable Manipulation</div></a>
                    K. Lu, <strong>B. Yang</strong>, B. Wang, A. Markham<br>
                    <em>IEEE International Conference on Robotics and Automation (ICRA)</em>, 2023
                    <br>
                    <a href="https://deerkk.github.io/DecoupleManiSkill">Project Page</a>
                    <p></p>
                    <p align="justify" style="font-size:13px">We propose a generalizable framework for robotic manipulation.</p>
                </div>
                <div class="vlar-clear"></div>
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        	<div class="vlar-pap-cont-item-c-si vlar-separatorLine">
            	<div class="vlar-pap-cont-item-c-si-img"><img src="vLAR/papers/22_neurips_ogc.gif"  ></div>
                <div class="vlar-pap-cont-item-c-si-c">
                        <p><a href="https://arxiv.org/abs/2210.04458">
                        <div class="papertitle">OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds</div></a>
                        Z. Song, <strong>B. Yang</strong><br>
                        <em>Advances in Neural Information Processing Systems (NeurIPS)</em>, 2022
                        <br>
                        <a href="https://arxiv.org/abs/2210.04458">arXiv</a> /
			            <a href="https://www.youtube.com/watch?v=dZBjvKWJ4K0">Video</a> /
                        <a href="https://github.com/vLAR-group/OGC"><font color="red">Code</font></a>
                        <iframe src="https://ghbtns.com/github-btn.html?user=vLAR-group&repo=OGC&type=star&count=true&size=small" frameborder="0" scrolling="0" width="120px" height="20px"></iframe>
                        <p></p>
                        <p align="justify" style="font-size:13px">We introduce the first unsupervised 3D object segmentation method on point clouds.</p>
                </div>
                <div class="vlar-clear"></div>
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        	<div class="vlar-pap-cont-item-c-si vlar-separatorLine">
            	<div class="vlar-pap-cont-item-c-si-img"><img src="vLAR/papers/22_neurips_unsupobjseg.gif"  ></div>
                <div class="vlar-pap-cont-item-c-si-c">
                        <p><a href="https://arxiv.org/abs/2210.02324">
                        <div class="papertitle">Promising or Elusive? Unsupervised Object Segmentation from Real-world Single Images</div></a>
                        Y. Yang, <strong>B. Yang</strong><br>
                        <em>Advances in Neural Information Processing Systems (NeurIPS)</em>, 2022
                        <br>
                        <a href="https://arxiv.org/abs/2210.02324">arXiv</a> /
			            <a href="https://vlar-group.github.io/UnsupObjSeg.html">Project Page</a> /
                        <a href="https://github.com/vLAR-group/UnsupObjSeg"><font color="red">Code</font></a>
                        <iframe src="https://ghbtns.com/github-btn.html?user=vLAR-group&repo=UnsupObjSeg&type=star&count=true&size=small" frameborder="0" scrolling="0" width="120px" height="20px"></iframe>
                        <p></p>
                        <p align="justify" style="font-size:13px">We systematically investigate the effectiveness of existing unsupervised models on challenging real-world images.</p>
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                <div class="vlar-clear"></div>
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        	<div class="vlar-pap-cont-item-c-si vlar-separatorLine">
            	<div class="vlar-pap-cont-item-c-si-img"><img src="vLAR/papers/22_eccv_sqn.gif"  ></div>
                <div class="vlar-pap-cont-item-c-si-c">
                        <p><a href="https://arxiv.org/abs/2104.04891">
                        <div class="papertitle">SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds</div></a>
                        Q. Hu, <strong>B. Yang*</strong>, G. Fang, Y. Guo, A. Leonardis, N. Trigoni, A. Markham<br>
                        <em>European Conference on Computer Vision (ECCV)</em>, 2022
                        <br>
                        <a href="https://arxiv.org/abs/2104.04891">arXiv</a> /
                        <a href="https://github.com/QingyongHu/SQN"><font color="red">Code</font></a>
                        <iframe src="https://ghbtns.com/github-btn.html?user=QingyongHu&repo=SQN&type=star&count=true&size=small" frameborder="0" scrolling="0" width="120px" height="20px"></iframe>
                        <br>(* indicates corresponding author)
                        <p align="justify" style="font-size:13px">We introduce a simple weakly-supervised neural network to learn precise 3D semantics for large-scale point clouds.</p>
                </div>
                <div class="vlar-clear"></div>
            </div>
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            <!--单条paper记录 begin-->
        	<div class="vlar-pap-cont-item-c-si vlar-separatorLine">
            	<div class="vlar-pap-cont-item-c-si-img"><img src="vLAR/papers/22_tpami_spinnet.png"  ></div>
                <div class="vlar-pap-cont-item-c-si-c">
                    <p><a href="https://ieeexplore.ieee.org/abstract/document/9792207">
                    <div class="papertitle">You Only Train Once: Learning General and Distinctive 3D Local Descriptors</div></a>
                    S. Ao, Y. Guo, Q. Hu, <strong>B. Yang</strong>, A. Markham, Z. Chen<br>
                    <em>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)</em>, 2022 <font color="red"><strong>(IF=16.39)</strong></font><br>
                    <a href="https://ieeexplore.ieee.org/abstract/document/9792207">IEEE Xplore</a> /
                    <a href="https://github.com/QingyongHu/SpinNet"><font color="red">Code</font></a>
                    <iframe src="https://ghbtns.com/github-btn.html?user=QingyongHu&repo=SpinNet&type=star&count=true&size=small" frameborder="0" scrolling="0" width="120px" height="20px"></iframe>
                    <p></p>
                    <p align="justify" style="font-size:13px">The journal version of our SpinNet. More experiments and analysis are included.</p>
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                <div class="vlar-clear"></div>
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            	<div class="vlar-pap-cont-item-c-si-img"><img src="vLAR/papers/22_arXiv_rangeUDF.gif"  ></div>
                <div class="vlar-pap-cont-item-c-si-c">
                    <p><a href="https://arxiv.org/abs/2204.09138">
                    <div class="papertitle">RangeUDF: Semantic Surface Reconstruction from 3D Point Clouds</div></a>
                    B. Wang, Z. Yu, <strong>B. Yang*</strong>, J. Qin, T. Breckon, L. Shao, N. Trigoni, A. Markham<br>
                    <a href="https://arxiv.org/abs/2204.09138">arXiv</a> /
                    <a href="https://www.youtube.com/watch?v=YahEnX1z-yw">Demo</a> /
                    <a href="https://github.com/vLAR-group/RangeUDF"><font color="red">Project page</font></a>
                    <iframe src="https://ghbtns.com/github-btn.html?user=vLAR-group&repo=RangeUDF&type=star&count=true&size=small" frameborder="0" scrolling="0" width="120px" height="20px"></iframe>
                    <br>(* indicates corresponding author)
                    <p align="justify" style="font-size:13px">We propose a new method to recover the geometry and semantics of continuous 3D scene surfaces from point clouds.</p>
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        	<div class="vlar-pap-cont-item-c-si vlar-separatorLine">
            	<div class="vlar-pap-cont-item-c-si-img"><img src="vLAR/papers/21_ijcv_sensaturban.gif"  ></div>
                <div class="vlar-pap-cont-item-c-si-c">
                    <p><a href="http://arxiv.org/abs/2009.03137">
                    <div class="papertitle">SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds</div></a>
                    Q. Hu, <strong>B. Yang*</strong>, S. Khalid, W. Xiao, N. Trigoni, A. Markham<br>
                    <em>International Journal of Computer Vision (IJCV)</em>, 2022 <font color="red"><strong>(IF=7.41)</strong></font><br>
                    <a href="https://arxiv.org/abs/2201.04494">arXiv</a> /
                    <a href="https://link.springer.com/article/10.1007/s11263-021-01554-9">Springer Access</a> /
                    <a href="https://www.youtube.com/watch?v=IG0tTdqB3L8">Demo</a> /
                    <a href="https://github.com/QingyongHu/SensatUrban"><font color="red">Project page</font></a>
                    <iframe src="https://ghbtns.com/github-btn.html?user=QingyongHu&repo=SensatUrban&type=star&count=true&size=small" frameborder="0" scrolling="0" width="120px" height="20px"></iframe>
                    <br>(* indicates corresponding author)
                    <p align="justify" style="font-size:13px">The journal version of our SensatUrban. More experiments and analysis are included.</p>
                </div>
                <div class="vlar-clear"></div>
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        	<div class="vlar-pap-cont-item-c-si vlar-separatorLine">
            	<div class="vlar-pap-cont-item-c-si-img"><img src="vLAR/papers/21_senj_pointloc.png"  ></div>
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                        <p><a href="https://arxiv.org/abs/2003.02392">
                        <div class="papertitle">PointLoc: Deep Pose Regressor for LiDAR Point Cloud Localization</div></a>
                        Wei Wang, Bing Wang, Peijun Zhao, Changhao Chen, Ronald Clark, <strong>B. Yang</strong>, Andrew Markham, Niki Trigoni <br>
                        <em>IEEE Sensor Journal</em>, 2022 <font color="red"><strong>(IF=3.30)</strong></font><br>
                        <a href="https://arxiv.org/abs/2003.02392">arXiv</a> /
                        <a href="https://ieeexplore.ieee.org/abstract/document/9617633">IEEE Xplore</a>
                        <p></p>
                        <p align="justify" style="font-size:13px">We present a learning-based LiDAR relocalization framework to efficiently estimate 6-DoF poses from LiDAR point clouds. </p>
                </div>
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        	<!--单条paper记录 begin-->
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                <div class="vlar-pap-cont-item-c-si-c ">
                        <p><a href="http://arxiv.org/abs/2010.04595">
                         <div class="papertitle">GRF: Learning a General Radiance Field for 3D Representation and Rendering</div></a>
                         A. Trevithick, <strong>B. Yang</strong> <br>
                         <em>IEEE International Conference on Computer Vision (ICCV)</em>, 2021
                         <br>
                         <a href="http://arxiv.org/abs/2010.04595">arXiv</a> /
                         <font color="red"> News:</font>
                         <a href="https://mp.weixin.qq.com/s/s2j9D-ovrU9WDt-aS9V-Iw"><font color="red">CVer</font></a> /
                         <a href="https://github.com/alextrevithick/GRF"><font color="red">Code</font></a>
                         <iframe src="https://ghbtns.com/github-btn.html?user=alextrevithick&repo=GRF&type=star&count=true&size=small" frameborder="0" scrolling="0" width="120px" height="20px"></iframe>
                        <p></p>
                        <p align="justify" style="font-size:13px">We introduce a simple implicit neural function to represent complex 3D geometries purely from 2D images.</p>

                </div>
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        	<!--单条paper记录 begin-->
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                <div class="vlar-pap-cont-item-c-si-c">
                        <p><a href="http://arxiv.org/abs/2010.04595">
                        <div class="papertitle">Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling</div></a>
                        Q. Hu, <strong>B. Yang*</strong>, L. Xie, S. Rosa, Y. Guo, Z. Wang, N. Trigoni, A. Markham<br>
                        <em>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)</em>, 2021 <font color="red"><strong>(IF=16.39)</strong></font><br>
                        <a href="https://arxiv.org/abs/2107.02389">arXiv</a> /
                        <a href="https://ieeexplore.ieee.org/document/9440696">IEEE Xplore</a> /
                        <a href="https://github.com/QingyongHu/RandLA-Net"><font color="red">Code</font></a>
                        <iframe src="https://ghbtns.com/github-btn.html?user=QingyongHu&repo=RandLA-Net&type=star&count=true&size=small"
                        frameborder="0" scrolling="0" width="100px" height="20px"></iframe>
                        <br>(* indicates corresponding author)
                        <p align="justify" style="font-size:13px">The journal version of our RandLA-Net. More experiments and analysis are included.</p>

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            </div>
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        	<!--单条paper记录 begin-->
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            	<div class="vlar-pap-cont-item-c-si-img"><img src="vLAR/papers/21_cvpr_spinnet.png"  ></div>
                <div class="vlar-pap-cont-item-c-si-c">
                    <p><a href="https://arxiv.org/abs/2011.12149">
                    <div class="papertitle">SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration</div></a>
                    S. Ao^, Q. Hu^, <strong>B. Yang</strong>, A. Markham, Y. Guo<br>
                    <em>IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</em>, 2021
                    <br>
                    <a href="https://arxiv.org/abs/2011.12149">arXiv</a> /
                    <a href="https://github.com/QingyongHu/SpinNet"><font color="red">Code</font></a>
                    <iframe src="https://ghbtns.com/github-btn.html?user=QingyongHu&repo=SpinNet&type=star&count=true&size=small" frameborder="0" scrolling="0" width="120px" height="20px"></iframe>
                    <br>(^ indicates equal contributions)
                    <p align="justify" style="font-size:13px">We introduce a simple and general neural network to register pieces of 3D point clouds.
                    </p>
                </div>
                <div class="vlar-clear"></div>
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            	<div class="vlar-pap-cont-item-c-si-img"><img src="vLAR/papers/21_cvpr_sensaturban.gif"  ></div>
                <div class="vlar-pap-cont-item-c-si-c">
                    <p><a href="http://arxiv.org/abs/2009.03137">
                    <div class="papertitle">Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges</div></a>
                    Q. Hu, <strong>B. Yang*</strong>, S. Khalid, W. Xiao, N. Trigoni, A. Markham<br>
                    <em>IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</em>, 2021
                    <br>
                        <!--<font color="red"><strong>..</strong></font><br>-->
                    <a href="http://arxiv.org/abs/2009.03137">arXiv</a> /
                    <a href="https://www.youtube.com/watch?v=IG0tTdqB3L8">Demo</a> /
                    <a href="https://github.com/QingyongHu/SensatUrban"><font color="red">Project page</font></a>
                    <iframe src="https://ghbtns.com/github-btn.html?user=QingyongHu&repo=SensatUrban&type=star&count=true&size=small" frameborder="0" scrolling="0" width="120px" height="20px"></iframe>
                    <br>(* indicates corresponding author)
                    <p align="justify" style="font-size:13px">We introduce an urban-scale photogrammetric point cloud dataset and extensively evaluate and analyze the state-of-the-art algorithms on the dataset.</p>
                </div>
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            	<div class="vlar-pap-cont-item-c-si-img"><img src="vLAR/papers/21_icra_radarloc.png"  ></div>
                <div class="vlar-pap-cont-item-c-si-c">
                        <p><a href="#">
                        <div class="papertitle">RadarLoc: Learning to Relocalize in FMCW Radar</div></a>
                        W. Wang, P.P.B. de Gusmao, <strong>B. Yang</strong>, A. Markham, N. Trigoni<br>
                        <em>IEEE International Conference on Robotics and Automation (ICRA) </em>, 2021
                        <br>
                        <a href="https://arxiv.org/abs/2103.11562">arXiv</a> /
                        <a href="https://ieeexplore.ieee.org/document/9560858">IEEE Xplore</a>
                        <p></p>
                        <p align="justify" style="font-size:13px">We introduce a simple end-to-end neural network with self-attention to estimate global poses from FMCW radar scans.</p>

                </div>
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            </div>
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        	<!--单条paper记录 begin-->
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            	<div class="vlar-pap-cont-item-c-si-img"><img src="vLAR/papers/20_cvpr_randlanet.gif"  ></div>
                <div class="vlar-pap-cont-item-c-si-c">
                        <p><a href="https://arxiv.org/abs/1911.11236">
                        <div class="papertitle">RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds</div></a>
                        Q. Hu, <strong>B. Yang*</strong>, L. Xie, S. Rosa, Y. Guo, Z. Wang, N. Trigoni, A. Markham
                        <br>
                        <em>IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</em>, 2020
                        <br>
                        <a href="https://arxiv.org/abs/1911.11236">arXiv</a> /
                        <a href="http://www.semantic3d.net/view_results.php">Semantic3D Benchmark</a> /
                        <font color="red"> News:</font>
                        <a href="https://mp.weixin.qq.com/s/k_oROm1Zr6l0YNKGELx3Bw"><font color="red">(新智元,</font></a>
                        <a href="https://mp.weixin.qq.com/s/Ed9v6I6l2tLTHmMW7B3O3g"><font color="red">AI科技评论,</font></a>
                        <a href="https://mp.weixin.qq.com/s/TTv6pSPjmdsEF4kvVY-ZzQ"><font color="red">CVer)</font></a> /
                        <a href="https://www.youtube.com/watch?v=Ar3eY_lwzMk"><font color="red">Video</font></a> /
                        <a href="https://github.com/QingyongHu/RandLA-Net"><font color="red">Code</font></a>
                        <iframe src="https://ghbtns.com/github-btn.html?user=QingyongHu&repo=RandLA-Net&type=star&count=true&size=small"
                         frameborder="0" scrolling="0" width="100px" height="20px"></iframe>
                        <br>(* indicates corresponding author)
                        <p align="justify" style="font-size:13px">We introduce an efficient and lightweight neural architecture to directly infer per-point semantics for large-scale point clouds. </p>
                </div>
                <div class="vlar-clear"></div>
            </div>
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        	<!--单条paper记录 begin-->
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            	<div class="vlar-pap-cont-item-c-si-img"><img src="vLAR/papers/19_neurips_3d_bonet.gif"  ></div>
                <div class="vlar-pap-cont-item-c-si-c">
                        <p><a href="https://arxiv.org/abs/1906.01140">
                        <div class="papertitle">Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds</div></a>
                        <strong>B. Yang</strong>, J. Wang, R. Clark, Q. Hu, S. Wang, A. Markham, N. Trigoni
                        <br>
                        <em>Advances in Neural Information Processing Systems (NeurIPS)</em>, 2019 <font color="red"><strong>(Spotlight, 200/6743)</strong></font>
                        <br>
                        <!--<font color="red"><strong>..</strong></font><br>-->
                        <a href="https://arxiv.org/abs/1906.01140">arXiv</a> /
                        <a href="http://kaldir.vc.in.tum.de/scannet_benchmark/result_details?id=118">ScanNet Benchmark</a> /
                        <a href="https://www.reddit.com/r/MachineLearning/comments/bx8jhz/r_new_sota_for_3d_object_detection/">Reddit Discussion</a> /
                        <font color="red"> News:</font>
                        <a href="https://mp.weixin.qq.com/s/jHbWf_SSZE_J6NRJR-96sQ"><font color="red">(新智元,</font></a>
                        <a href="https://mp.weixin.qq.com/s/4GPkmTri4Vk7Xy0J8TiBNw"><font color="red">图像算法,</font></a>
                        <a href="https://mp.weixin.qq.com/s/C1FDPkAkmnmAZ_gvvtzBHw"><font color="red">AI科技评论,</font></a>
                        <a href="https://mp.weixin.qq.com/s/wViZITtsb4j3oFtOpJI9wQ"><font color="red">将门创投,</font></a>
                        <a href="https://mp.weixin.qq.com/s/S7mHrOxOwTIhDGPhu1SI4A"><font color="red">CVer,</font></a>
                        <a href="https://mp.weixin.qq.com/s/gybhVw3D4ykAHsVGzazWNw"><font color="red">泡泡机器人)</font></a> /
                        <a href="https://www.youtube.com/watch?v=Bk727Ec10Ao"><font color="red">Video</font></a> /
                        <a href="https://github.com/Yang7879/3D-BoNet"><font color="red">Code</font></a>
                        <iframe src="https://ghbtns.com/github-btn.html?user=Yang7879&repo=3D-BoNet&type=star&count=true&size=small"
                        frameborder="0" scrolling="0" width="100px" height="20px"></iframe>
                       <p></p>
                        <p align="justify" style="font-size:13px">We propose a simple and efficient neural architecture for accurate 3D instance segmentation on point clouds.
                         It achieves the SOTA performance on ScanNet and S3DIS (June 2019).</p>

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        	<!--单条paper记录 begin-->
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            	<div class="vlar-pap-cont-item-c-si-img"><img src="vLAR/papers/19_iros_deeppco.jpg"  ></div>
                <div class="vlar-pap-cont-item-c-si-c">
                        <p><a href="https://arxiv.org/abs/1910.11088">
                        <div class="papertitle">DeepPCO: End-to-End Point Cloud Odometry through Deep Parallel Neural Network</div></a>
                        W. Wang,  M.R.U. Saputra, P. Zhao, P. Gusmao, <strong>B. Yang</strong>, C. Chen, A. Markham, N. Trigoni<br>
                        <em>IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)</em>, 2019 <br>
                        <a href="https://arxiv.org/abs/1910.11088">arXiv</a> /
                        <a href="https://ieeexplore.ieee.org/abstract/document/8967756">IEEE Xplore</a>
                        <p></p>
                        <p align="justify" style="font-size:13px">We propose a novel end-to-end deep parallel neural network to estimate the 6-DOF poses using consecutive 3D point clouds.</p>
                </div>
                <div class="vlar-clear"></div>
            </div>
			<!--单条paper记录 end-->
        	<!--单条paper记录 begin-->
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            	<div class="vlar-pap-cont-item-c-si-img"><img src="vLAR/papers/19_ijcv_attsets.jpg"  ></div>
                <div class="vlar-pap-cont-item-c-si-c">
                        <p><a href="https://link.springer.com/article/10.1007/s11263-019-01217-w">
                        <div class="papertitle">Robust Attentional Aggregation of Deep Feature Sets for Multi-view 3D Reconstruction</div></a>
                        <strong>B. Yang</strong>, S. Wang, A. Markham, N. Trigoni<br>
                        <em>International Journal of Computer Vision (IJCV)</em>, 2019 <font color="red"><strong>(IF=6.07)</strong></font><br>
                        <a href="https://arxiv.org/abs/1808.00758">arXiv</a> /
                        <a href="https://link.springer.com/article/10.1007/s11263-019-01217-w">Springer Open Access</a> /
                        <a href="https://github.com/Yang7879/AttSets"><font color="red">Code</font></a>
                        <iframe src="https://ghbtns.com/github-btn.html?user=Yang7879&repo=AttSets&type=star&count=true&size=small"
                        frameborder="0" scrolling="0" width="120px" height="20px"></iframe>
                        <p></p>
                        <p align="justify" style="font-size:13px"> We propose an attentive aggregation module together
                            with a training algorithm for multi-view 3D object reconstruction.</p>
                </div>
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            	<div class="vlar-pap-cont-item-c-si-img"><img src="vLAR/papers/19_cvprw_embeddings.jpg"  ></div>
                <div class="vlar-pap-cont-item-c-si-c">
                        <p><a href="http://openaccess.thecvf.com/content_CVPRW_2019/html/Explainable_AI/Lin_Learning_Semantically_Meaningful_Embeddings_Using_Linear_Constraints_CVPRW_2019_paper.html">
                        <div class="papertitle">Learning Semantically Meaningful Embeddings Using Linear Constraints</div></a>
                        S. Lin, <strong>B. Yang</strong>, R. Birke, R. Clark<br>
                        <em>IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR-W)</em>, 2019<br>
                        <a href="http://openaccess.thecvf.com/content_CVPRW_2019/html/Explainable_AI/Lin_Learning_Semantically_Meaningful_Embeddings_Using_Linear_Constraints_CVPRW_2019_paper.html">CVF Open Access</a>
                        <p></p>
                        <p align="justify" style="font-size:13px">We propose a simple embedding learning method that jointly optimises for an auto-encoding reconstruction task
                            and for estimating the corresponding attribute labels.</p>
                </div>
                <div class="vlar-clear"></div>
            </div>
			<!--单条paper记录 end-->
        	<!--单条paper记录 begin-->
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            	<div class="vlar-pap-cont-item-c-si-img"><img src="vLAR/papers/18_tpami_3d_recgan++.png"  ></div>
                <div class="vlar-pap-cont-item-c-si-c">
                       <p><a href="https://arxiv.org/abs/1802.00411">
                       <div class="papertitle">Dense 3D Object Reconstruction from a Single Depth View</div></a>
                       <strong>B. Yang</strong>, S. Rosa, A. Markham, N. Trigoni, H. Wen<br>
                       <em>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)</em>, 2018 <font color="red"><strong>(IF=17.73)</strong></font><br>
                       <a href="https://arxiv.org/abs/1802.00411">arXiv</a> /
                       <a href="https://ieeexplore.ieee.org/abstract/document/8453803">IEEE Xplore</a> /
                       <a href="https://github.com/Yang7879/3D-RecGAN-extended"><font color="red">Code</font></a>
                       <iframe src="https://ghbtns.com/github-btn.html?user=Yang7879&repo=3D-RecGAN-extended&type=star&count=true&size=small"
                       frameborder="0" scrolling="0" width="120px" height="20px"></iframe>
                       <p></p>
                       <p align="justify" style="font-size:13px">We propose a novel neural architecture to reconstruct the complete 3D structure of a given object
                           from a single arbitrary depth view using generative adversarial networks.</p>
                </div>
                <div class="vlar-clear"></div>
            </div>
			<!--单条paper记录 end-->
        	<!--单条paper记录 begin-->
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            	<div class="vlar-pap-cont-item-c-si-img"><img src="vLAR/papers/18_ijcai_3d_physnet.gif"  ></div>
                <div class="vlar-pap-cont-item-c-si-c">
                           <p><a href="https://arxiv.org/abs/1805.00328">
                           <div class="papertitle">3D-PhysNet: Learning the Intuitive Physics of Non-Rigid Object Deformations</div></a>
                           Z. Wang, S. Rosa, <strong>B. Yang</strong>, S. Wang, N. Trigoni, A. Markham<br>
                           <em>International Joint Conference on Artificial Intelligence (IJCAI)</em>, 2018 <br>
                           <a href="https://arxiv.org/abs/1805.00328">arXiv</a> /
                           <a href="https://github.com/vividda/3D-PhysNet"><font color="red">Code</font> </a>
                           <iframe src="https://ghbtns.com/github-btn.html?user=vividda&repo=3D-PhysNet&type=star&count=true&size=small"
                           frameborder="0" scrolling="0" width="120px" height="20px"></iframe>
                           <p></p>
                           <p align="justify" style="font-size:13px">We present a neural framework to predict how a 3D object will deform
                               under an applied force using intuitive physics modelling.</p>
                </div>
                <div class="vlar-clear"></div>
            </div>
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                <div class="vlar-pap-cont-item-c-si-c">
                           <p><a href="http://openaccess.thecvf.com/content_cvpr_2018_workshops/w9/html/Yang_Learning_3D_Scene_CVPR_2018_paper.html">
                           <div class="papertitle">Learning 3D Scene Semantics and Structure from a Single Depth Image</div></a>
                           <strong>B. Yang*</strong>, Z. Lai*, X. Lu, S. Lin, H. Wen, A. Markham, N. Trigoni<br>
                           <em>IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR-W)</em>, 2018 <br>
                           <a href="http://openaccess.thecvf.com/content_cvpr_2018_workshops/w9/html/Yang_Learning_3D_Scene_CVPR_2018_paper.html">CVF Open Access</a> /
                           <a href="https://ieeexplore.ieee.org/abstract/document/8575531">IEEE Xplore</a>
                           <br>(* indicates equal contribution)
                           <p></p>
                           <p align="justify" style="font-size:13px">We propose an efficient and holistic pipeline to simultaneously learn
                               the semantics and structure of a scene from a single depth image.</p>
                </div>
                <div class="vlar-clear"></div>
            </div>
			<!--单条paper记录 end-->
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            	<div class="vlar-pap-cont-item-c-si-img"><img src="vLAR/papers/18_icra_defonet.jpg"  ></div>
                <div class="vlar-pap-cont-item-c-si-c">
                           <p><a href="https://arxiv.org/abs/1804.05928">
                           <div class="papertitle">Defo-Net: Learning Body Deformation Using Generative Adversarial Networks</div></a>
                           Z. Wang, S. Rosa, L. Xie, <strong>B. Yang</strong>, S. Wang, N. Trigoni, A. Markham<br>
                           <em>IEEE International Conference on Robotics and Automation (ICRA) </em>, 2018 <br>
                           <a href="https://arxiv.org/abs/1804.05928">arXiv</a> /
                           <a href="https://ieeexplore.ieee.org/abstract/document/8462832">IEEE Xplore</a> /
                           <a href="https://www.youtube.com/watch?v=noG5DDX3coQ"><font color="red">Video</font></a> /
                           <a href="https://github.com/vividda/Defo-Net"><font color="red">Code</font> </a>
                           <iframe src="https://ghbtns.com/github-btn.html?user=vividda&repo=Defo-Net&type=star&count=true&size=small"
                           frameborder="0" scrolling="0" width="120px" height="20px"></iframe>
                           <p></p>
                           <p align="justify" style="font-size:13px">We present a novel generative adversarial network to predict
                               body deformations under external forces from a single RGB-D image.</p>
                </div>
                <div class="vlar-clear"></div>
            </div>
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        	<!--单条paper记录 begin-->
        	<div class="vlar-pap-cont-item-c-si">
            	<div class="vlar-pap-cont-item-c-si-img"><img src="vLAR/papers/17_iccvw_3d_recgan.jpg"  ></div>
                <div class="vlar-pap-cont-item-c-si-c">
                           <p><a href="https://arxiv.org/abs/1708.07969">
                           <div class="papertitle">3D Object Reconstruction from a Single Depth View with Adversarial Learning</div></a>
                           <strong>B. Yang</strong>, H. Wen, S. Wang, R. Clark, A. Markham, N. Trigoni<br>
                           <em>IEEE International Conference on Computer Vision Workshops (ICCV-W) </em>, 2017 <br>
                           <a href="https://arxiv.org/abs/1708.07969">arXiv</a> /
                           <a href="https://ieeexplore.ieee.org/abstract/document/8265295">IEEE Xplore</a> /
                           <a href="https://mp.weixin.qq.com/s?__biz=MzA3MzI4MjgzMw==&mid=2650730434&idx=4&sn=4a03526f020f30cc65b52976bb56f352&scene=0"><font color="red"> News: 机器之心</font></a> /
                           <a href="https://github.com/Yang7879/3D-RecGAN"><font color="red">Code</font> </a>
                           <iframe src="https://ghbtns.com/github-btn.html?user=Yang7879&repo=3D-RecGAN&type=star&count=true&size=small"
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                           <p></p>
                           <p align="justify" style="font-size:13px">We propose a novel approach to reconstruct the complete 3D structure of a given
                               object from a single arbitrary depth view using generative adversarial networks.</p>
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