ConTesse: Accurate Occluding Contours for Subdivision Surfaces

Chenxi Liu¹, Pierre Bénard², Aaron Hertzmann³, Shayan Hoshyari⁴

¹University of British Columbia, ²Univ.Bordeaux, CNRS, Bordeaux INP, INRIA, LaBRI, UMR 5800, ³Adobe Research, ⁴Adobe

This repository contains the source code for the paper ConTesse: Accurate Occluding Contours for Subdivision Surfaces.

• src contains the source code of ConTesse.
• py contains Python helper scripts to run on given models.
• cmake contains cmake files to fetch external libraries.
• external contains external codes.

The data (configuration files, camera files, obj models) is included in data; test models from Computing Smooth Surface Contours with Accurate Topology can be downloaded from here.

Building

1. Setup contess development env by running

   conda env update --file conda_contesse.yaml
   conda activate contess
   

2. Build the code

   mkdir build && cd build
   cmake .. -DCMAKE_BUILD_TYPE=Release
   # Build using the selected generator

Running our method

Run individual test cases

An individual test case is defined by a given obj model and a camera setting in a json file. For instance,

{
  "camera_info": {
    "index": 1,
    "value": {
      "camera_x": 0.0,
      "camera_y": 0.0,
      "camera_z": 0.0,
      "full_info": false,
      "lookat": [
        0.0,
        0.0,
        0.0
      ]
    }
  },
  "description": "Opensubdiv torus quad view 1: subdivided",
  "model_name": "torus_quad_v1",
  "model_path": "torus_quad"
  "subdiv_level": 1
}

Here model_path is relative to data/meshes folder in this repo. Configuration json files of input models used in our paper can be found in data/json (pipeline contains static models; bunny-p1, bunny-p2, walking, Angela contains test sequences from [Bénard et al. 2014]).

To run an individual test case, call, for instance,

# From build directory
python3 ../py/scripts/run_model.py ../data/json/pipeline/torus_quad_v1.json ./TestsContourTessellation --out [output_folder]

The resulting files torus_quad_v1_out.svg (ConTesse output), torus_quad_v1_trivial.svg (comparison output by trivial ray casting visibility test) are generated into [output_folder].

Run test cases in batch

Multiple test cases defined by individual json files can be run in batch mode. To define a batch of inputs, use a json file, for instance,

{
  "multicamera_test_cases": [
    "pipeline/split.json",
    "pipeline/torus_quad_v1.json"
  ],
  "pipeline_test_cases": [
    "pipeline/ribbon_open.json"
  ]
}

Here multicamera_test_cases automatically test 26 camera positions centered to the model in a grid; pipeline_test_cases uses the camera setting given in the individual json files.

To run multiple test cases in batch mode, call, for instance,

# From build directory
python3 ../py/scripts/run_model.py [batch_json] ./TestsContourTessellation --max-procs [max_number_of_processes] --out [output_folder]

The resulting files are generated with maximum [max_number_of_processes] processes into [output_folder].

License

The source code (everything under src and py) is licensed under Version 2.0 of the Apache License. The input models (which need to be downloaded separately) are licensed under separate licenses. Please refer to license.yml for license information for each model.

BibTeX

@article{ConTesse,
  title = {ConTesse: Accurate Occluding Contours for Subdivision Surfaces},
  author = {Liu, Chenxi and B\'{e}nard, Pierre and Hertzmann, Aaron and Hoshyari, Shayan},
  year = {2023},
  journal = {ACM Trans. Graph.},
  month = feb,
  volume = {42},
  number = {1},
  articleno = {5},
  url = {https://doi.org/10.1145/3544778},
  doi = {10.1145/3544778}
}