PoNQ: a Neural QEM-based Mesh Representation [CVPR 2024]

Nissim Maruani¹, Maks Ovsjanikov², Pierre Alliez¹, Mathieu Desbrun³.

¹ Inria, Université Côte d’Azur   ² LIX, École Polytechnique, IP Paris   ² Inria Saclay, École Polytechnique

Please see our project page for video presentation & 3D meshes.

News

• 2024-03 Accepted at CVPR 2024 🚀🚀🚀

Requirements

The code is tested on the listed versions but other versions may also work:

• Python 3.9
• PyTorch 1.12.1
• PyTorch3D 0.7.0
• trimesh 3.15.5
• igl 2.2.1
• tqdm 4.64.1
• yaml 0.2.5
• scikit-learn 0.19.3

Demos

To interactively test our method, please refer to the notebooks in src/demos/.

Iso-surfacing of SDF grids: demo_learning.ipynb

Our PoNQ model pre-trained on ABC can reconstruct 3D meshes from ground truth Signed Distance Fields (SDF) grids: it is an alternative to Marching Cubes. For high resolutions (or small GPUs), the input grid can be split: see demo_learning_large.ipynb.

Optimization-based fitting : demo_optimization.ipynb

Optimization of a PoNQ model with ground truth points and normals (no learning here). Can be used to compare with SIREN.

Optimization-based reconstruction of open models: demo_boundary.ipynb

Optimization of a PoNQ model to reconstruct an open shape.

Evaluation

Note that we provide the generated learning-based PoNQ meshes here. To reproduce our results, please first download the data for ABC and Thingi. The following command will generate meshes on both Thingi32 and ABC for PoNQ and PoNQ-lite with our pre-trained network:

mkdir out 
python src/generate_all_CNN.py configs/eval_cnn.yaml  # PoNQ
python src/generate_all_CNN.py configs/eval_cnn.yaml -subd 1 # PoNQ-lite

You can compute various metrics (CD, F1, NC, ECD, EF1) with:

python src/eval/eval_all.py configs/eval_cnn.yaml # PoNQ
python src/eval/eval_all.py configs/eval_cnn.yaml -subd 1 # PoNQ-lite

To check watertightness and count mesh elements, either use the compiled self_intersect or compile it yourself (make sure you have CGAL installed):

cd src/cpp_utils/build
rm self_intersect
cmake ../ # MAKE SURE YOU HAVE CGAL
make

Then run:

python src/eval/check_watertight.py FOLDER

Direct Optimization

The following command generates optimization-based PoNQ models on Thingi:

python src/utils/direct.py configs/direct_thingi.yaml -grid_n 32
python src/utils/direct.py configs/direct_thingi.yaml -grid_n 64
python src/utils/direct.py configs/direct_thingi.yaml -grid_n 128

You can compute various metrics (CD, F1, NC, ECD, EF1) with:

python src/eval/eval_THINGI.py FOLDER

Model training

Data preparation

Please follow the instructions provided by NMC to:

• Download the first chunk of ABC
• Run simplify_obj.py
• Compile SDFGen

After that, run get_data.py to obtain SDF grids, sampled points and sample normals for training.

Training

Run each one of the training phase, and do not forget to rename model.pt between each command:

cd learning
python src/utils/train_cnn_multiple_quadrics_split.py configs/abc_cnn_multiple_quadrics_split_1.yaml
python src/utils/train_cnn_multiple_quadrics_split.py configs/abc_cnn_multiple_quadrics_split_2.yaml
python src/utils/train_cnn_multiple_quadrics_split.py configs/abc_cnn_multiple_quadrics_split_3.yaml

Acknowledgments

This work was supported by 3IA Côte d'Azur (ANR-19-P3IA-0002), ERC Starting Grant 758800 (EXPROTEA), ERC Consolidator Grant 101087347 (VEGA), ANR AI Chair AIGRETTE, Ansys, Adobe Research, and a Choose France Inria chair.