Approximate Convex Decomposition for 3D Meshes with Collision-Aware Concavity and Tree Search [SIGGRAPH2022]

[project] [paper]

Approximate convex decomposition enables efficient geometry processing algorithms specifically designed for convex shapes (e.g., collision detection). We propose a method that is better to preserve collision conditions of the input shape with fewer components. It thus supports delicate and efficient object interaction in downstream applications.

Usage

(1) Clone the code

git clone --recurse-submodules https://github.com/SarahWeiii/CoACD.git

(2) Compile

cd CoACD
mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make

(3) Quick start

We provide a set of default parameters, and you only need to specify the input and output path. You can take an arbitrary mesh as input (in .obj format, no need to be a manifold) and run the algorithm by the following command:

./main -i PATH_OF_YOUR_MESH -o PATH_OF_OUTPUT

The running log and the generated convex components (in both .obj and .wrl formats) will be saved in PATH_OF_OUTPUT.

Parameters

Here is the description of the parameters (sorted by importance).

-i/--input: path for input mesh (.obj).
-o/--output: path for output (.obj or .wrl).
-l/--log: path for output logfile, default = same_path_as_output_components.
-t/--threshold: concavity threshold for terminating the decomposition (0.01~1), default = 0.05.
-np/--no-prerpocess: flag to disable manifold preprocessing, default = false. If your input is already manifold mesh, disabling the preprocessing can avoid introducing extra artifacts.
-nm/--no-merge: flag to disable merge postprocessing, default = false.
-mi/--mcts-iteration: number of search iterations in MCTS (60~2000), default = 100.
-md/--mcts-depth: max search depth in MCTS (2~7), default = 3.
-mn/--mcts-node: max number of child nodes in MCTS (10~40), default = 20.
-pr/--prep-resolution: resolution for manifold preprocess (1e3~1e5), default = 1e4.
-r/--resolution: sampling resolution for Hausdorff distance calculation (1e3~1e4), default = 2000.
-k: value of $k$ for $\operatorname{R_v}$ calculation, default = 0.3.
--pca: flag to enable PCA pre-processing, default = false.
--seed: random seed used for sampling, default = random().

A example of changing the parameters:

./main -i PATH_OF_YOUR_MESH -o PATH_OF_OUTPUT -t 0.05 -mi 200 -md 4 -mn 25

Parameter tuning tricks:

In most cases, you only need to adjust the threshold (0.01~1) to balance the level of details and the number of decomposed components. A higher value gives coarser results, and a lower value gives finer-grained results. You can refer to Fig. 14 in our paper for more details.
The default parameters are fast versions. If you care less about running time but more about the number of components, try to increase searching depth (-md), searching node (-mn) and searching iteration (-mi) for better cutting strategies.
-pr controls the quality of manifold preprocessing. A larger value can make the preprocessed mesh closer to the original mesh but also lead to more triangles and longer runtime.
Make sure your input mesh is 2-manifold solid if you want to use the -np flag. Skipping manifold pre-processing can better preserve input details, but please don't specify the -np flag if your input mesh is non-manifold (the algorithm may crush).
--seed is used for reproduction of same results as our algorithm is stochastic.

Examples

We provide some example meshes and a run_example.sh, and the results will be saved in the outputs folder.

bash run_example.sh

You can adjust the threshold by -t to see results with different quality.
Three of the examples are from PartNet-M (Bottle.obj, Kettle.obj, KitchenPot.obj), which are non-manifold. Two of them are from Thingi10K (Octocat-v2.obj, SnowFlake.obj), which are both 2-manifold.

Citation

If you find our code helpful, please cite our paper:

@article{wei2022approximate,
  title={Approximate Convex Decomposition for 3D Meshes with Collision-Aware Concavity and Tree Search},
  author={Wei, Xinyue and Liu, Minghua and Ling, Zhan and Su, Hao},
  journal={arXiv preprint arXiv:2205.02961},
  year={2022}
}

nikepupu / CoACD