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This repository contains the official implementation of
[ICCV 2023] Diffusion-SDF: Conditional Generative Modeling of Signed Distance Functions
Gene Chou, Yuval Bahat, Felix Heide
If you find our code or paper useful, please consider citing
@inproceedings{chou2022diffusionsdf,
title={Diffusion-SDF: Conditional Generative Modeling of Signed Distance Functions},
author={Gene Chou and Yuval Bahat and Felix Heide},
journal={The IEEE International Conference on Computer Vision (ICCV)},
year={2023}
}root directory
├── config
│ └── // folders for checkpoints and training configs
├── data
│ └── // folders for data (in csv format) and train test splits (json)
├── models
│ ├── // models and lightning modules; main model is 'combined_model.py'
│ └── archs
│ └── // architectures such as PointNets, SDF MLPs, diffusion network..etc
├── dataloader
│ └── // dataloaders for different stages of training and generation
├── utils
│ └── // reconstruction and evaluation
├── metrics
│ └── // reconstruction and evaluation
├── diff_utils
│ └── // helper functions for diffusion
├── environment.yml // package requirements
├── train.py // script for training, specify the stage of training in the config files
├── test.py // script for testing, specify the stage of testing in the config files
└── tensorboard_logs // created when running any training script
We recommend creating an anaconda environment using our provided environment.yml:
conda env create -f environment.yml
conda activate diffusionsdf
For training, we preprocess all meshes and store query coordinates and signed distance values in csv files. Each csv file corresponds to one object, and each line represents a coordinate followed by its signed distance value. See data/acronym for examples. Modify the dataloader according to your file format.
When sampling query points, make sure to also sample uniformly within the 3D grid space (i.e. from (-1,-1,-1) to (1,1,1)) rather than only sampling near the surface to avoid artifacts. For each training batch, we take 70% of query points sampled near the object surface and 30% sampled uniformly in the grid. grid_source in our dataloader and config file refers to the latter.
As described in our paper, there are three stages of training. All corresponding config files can be found in the config folders. Logs are created in a tensorboard_logs folder in the root directory. We recommend tuning the "kld_weight" when training the joint SDF-VAE model as it enforces the continuity of the latent space. A higher value (e.g. 0.1) will result in better interpolation and generalization but sometimes more artifacts. A lower value (e.g. 0.00001) will result in worse interpolation but higher quality of generations.
- Training SDF modulations
python train.py -e config/stage1_sdf/ -b 32 -w 8 # -b for batch size, -w for workers, -r to resume training
Training notes: For Acronym / ShapeNet datasets, the loss should go down to "latent_dim" in "SdfModelSpecs" listed in the config file.
- Training the diffusion model using the modulations extracted from the first stage
# extract the modulations / latent vectors, which will be saved in a "modulations" folder in the config directory
# the folder needs to correspond to "data_path" in the diffusion config files
python test.py -e config/stage1_sdf/ -r last
# unconditional
python train.py -e config/stage2_diff_uncond/ -b 32 -w 8
# conditional
python train.py -e config/stage2_diff_cond/ -b 32 -w 8
Training notes: When extracting modulations, we recommend filtering based on the chamfer distance. See test_modulations() in test.py for details. Some notes on the conditional config file: "perturb_pc":"partial", "crop_percent":0.5, and "sample_pc_size":128 refers to cropping 50% of a point cloud with 128 points to use as condition. dim in diffusion_model_specs needs to be the dimension of the latent vector, which is 3 times "latent_dim" in "SdfModelSpecs".
- End-to-end training using the saved models from above
# unconditional
python train.py -e config/stage3_uncond/ -b 32 -w 8 -r finetune # training from the saved models of first two stages
python train.py -e config/stage3_uncond/ -b 32 -w 8 -r last # resuming training if third stage has been trained
# conditional
python train.py -e config/stage3_cond/ -b 32 -w 8 -r finetune # training from the saved models of first two stages
python train.py -e config/stage3_cond/ -b 32 -w 8 -r last # resuming training if third stage has been trained
Training notes: The config file needs to contain the saved checkpoints for the previous two stages of training. The sdf loss (not generated sdf loss) should approach
- Testing SDF reconstructions and saving modulations
After the first stage of training, visualize / test reconstructions and save modulations:
# extract the modulations / latent vectors, which will be saved in a "modulations" folder in the config directory
# the folder needs to correspond to "data_path" in the diffusion config files
python test.py -e config/stage1_sdf/ -r last
A recon folder in the config directory will contain the .ply reconstructions and a cd.csv file that logs Chamfer Distance (CD). A modulation folder will contain latent.txt files for each SDF. The modulation folder will be the data path to the second stage of training.
- Generations
Meshes can be generated after the second or third stage of training.
python test.py -e config/stage3_uncond/ -r finetune # generation after second stage
python test.py -e config/stage3_uncond/ -r last # after third stage
A recon folder in the config directory will contain the .ply reconstructions. max_batch arguments in test.py are used for running marching cubes; change it to the max value your GPU memory can hold.
We adapt code from
GenSDF https://github.com/princeton-computational-imaging/gensdf
DALLE2-pytorch https://github.com/lucidrains/DALLE2-pytorch
Convolutional Occupancy Networks https://github.com/autonomousvision/convolutional_occupancy_networks (for PointNet encoder)
Multimodal Shape Completion via cGANs https://github.com/ChrisWu1997/Multimodal-Shape-Completion (for conditional metrics)
PointFlow https://github.com/stevenygd/PointFlow (for unconditional metrics)