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OptCuts, a new parameterization algorithm, jointly optimizes arbitrary embeddings for seam quality and distortion. OptCuts requires no parameter tuning; automatically generating mappings that minimize seam-lengths while satisfying user-requested distortion bounds.

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OptCuts

OptCuts, a new parameterization algorithm, jointly optimizes arbitrary embeddings for seam quality and distortion. OptCuts requires no parameter tuning; automatically generating mappings that minimize seam-lengths while satisfying user-requested distortion bounds.

http://www.cs.ubc.ca/labs/imager/tr/2018/OptCuts/

Clone Repository

git clone https://github.com/liminchen/OptCuts

Then in OptCuts folder there will be

  • src/: source code
  • ext/: external libraries including Intel TBB and libigl (an older version, with Eigen, OpenGL, GLFW, libpng, and Triangle)
  • display/: html code for display results
  • build.py: a python script to automatically build OptCuts using cmake
  • batch.py: a python script to automatically run a batch of examples provided under input/
  • batch_RSP.py: a python script to automatically run a batch of regional seam placement examples provided under input/RSP/
  • display.py: a python script to automatically generate html files for display results
  • input/: input meshes including the 71 input meshes from our benchmark
  • output/: (will be created) contains output of each input in separate subfolders
  • CMakeLists.txt and cmake/: cmake files

Compile and Run

  • Build
cd OptCuts
python build.py

Tips on linear solver: By default OptCuts is built with Eigen::SimplicialLDLT. However, we also provide interfaces for CHOLMOD and PARDISO under src/LinSysSolver/ for customization. But note that unless MKL BLAS and LAPACK are used, CHOLMOD or PARDISO built with openblas on linux performs similar to Eigen.

Tips for Windows users: Compiling OptCuts on Windows may need manually setting up the environment. Running OptCuts on Windows is possible to encounter severe speed issues, which can be related to the memory management of Eigen backend. A useful suggestion is to swap out Eigen's malloc with dlmalloc.

  • Run
python batch.py

This will run OptCuts on all the triangle meshes directly under input/, where by default bimba_i_f10000.obj will be processed as a "hello world" example with a visualization window (mode 10).

Tips on initial UV map: If the input mesh does not have texture coordinates, OptCuts will automatically compute default initial UV map, or OptCuts will start with the UV map provided in the input file.

Tips on input mesh: OptCuts takes input meshes with only one connected component. For meshes with multiple connected components, OptCuts can be independently applied on each of them.

We also provide a python script for automatically running the two regional seam placement examples in our paper

python batch_RSP.py

This will run OptCuts with all the input meshes provided under input/RSP. To enable regional seam placement, a text file named inputMeshName_selected.txt containing the indices of all selected vertices in the salient regions must be present for each input mesh under the same directory.

  • Display

After finish running OptCuts, the results will be saved under output/ with separate folders per input. Then you can do

python display.py

to generate html files that will display all the completed results in output/ and open display/display.html to view them.

Output Files

For each input, OptCuts will create a folder under output/ to hold all the output files. The folder name will be composed of the input command line arguments, thus results with the same input and setting will be overwritten into the same folder. The output contains

  • 0.png: initial UV map, colored by distortion (blue -> green: highly distorted -> isometric)
  • anim.gif: UV map changes during optimization process, colored by distortion
  • finalResult.png: output UV map, colored by distortion
  • finalResult_mesh.obj: input model with output UV in the original scale
  • finalResult_mesh_normalizedUV.obj: input model with output UV scaled to [0, 1]^2
  • 3DView0_distortion.png: input model visualized with checkerboard texture and distortion color map
  • 3DView0_seam.png: input model visualized with seams and importance if regional seam placement is requested
  • energyValPerIter.txt: energy value of Ew, Ed, Es, and lambda of each inner iteration
  • gradientPerIter.txt: energy gradient of Ed of each inner iteration
  • info.txt: parameterization results quality output for webpage visualization
  • log.txt: debug info

Command Line Arguments

Format: progName mode inputMeshPath lambda_init testID methodType distortionBound useBijectivity initialCutOption [anyStringYouLike]

Example: ./build/OptCuts_bin 10 input/bimba_i_f10000.obj 0.999 1 0 4.1 1 0 firstTrial

  • progName
    • ./build/OptCuts_bin
  • mode
    • 0: OptCuts with real-time optimization mode, UV coordinates change in each inner iteration will be visualized, need user to start/restart and pause the process via '/' key
    • 10: OptCuts with offline optimization mode, only UV cooridinates change after each alternation between geometry and topology step will be visualized
    • 100: OptCuts with headless mode, no visualization, only command line prints and text file output
    • 1: diagnostic mode, for unit test
    • 2: mesh processing mode, for unit test
  • inputMeshPath
    • can be either absolute path or relative path to global variable meshFolder in main.cpp
    • .obj and .off are supported
  • lambda_init
    • 0: minimize Symmetric Dirishlet energy with initial cuts, methodType must also be set to 4
    • (0,1): joint optimization. OptCuts start with 0.999 and iteratively update it according to the distortions of intermediate UV maps; 0.025 works generally well for OptCuts with fixed lambda (no dual step) on all inputs.
  • testID (initial homotopy parameter)
    • It serves as a classification ID for our visualization webpage
  • methodType
    • 0: OptCuts, must also have 0 < lambda < 1
    • 1: EBCuts, using the extremity-boundary cutting strategy from Geometry Images within OptCuts framework
    • 2: OptCuts with fixed lambda, must also have 0 < lambda < 1
    • 3: Ed minimization with the initial seams
  • distortionBound
    • (4, inf): distortion bound bd on Symmetric Dirichlet energy Ed
  • useBijectivity
    • 0: no bijectivity
    • 1: enforce bijectivity
  • initialCutOption
    • 0: random one-point initial cut for genus-0 closed surfaces
    • 1: farthest two-point initial cut for genus-0 closed surfaces
  • anyStringYouLike
    • optional, the appended string to the name of a folder to be created for holding all output files

Keyboard Events

  • '/': start/restart or pause the optimization - in offline optimization mode (mode 10), optimization is started with the program, while in real-time optimization mode (mode 0), optimization needs to be started by the user.
  • '0': view input model/UV
  • '1': view current model/UV
  • 'u': toggle viewing model or UV, default is UV
  • 'd': toggle distortion visualization, default is ON
  • 'c': toggle checkerboard texture visualization, default is OFF
  • 's': toggle seam visualization, default is ON
  • 'b': toggle lighting, default is OFF
  • 'p': toggle viewing seam corners (drawn as black dots), default is ON
  • 'o': take a screenshot and save the model with current UV

About

OptCuts, a new parameterization algorithm, jointly optimizes arbitrary embeddings for seam quality and distortion. OptCuts requires no parameter tuning; automatically generating mappings that minimize seam-lengths while satisfying user-requested distortion bounds.

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  • C++ 89.4%
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