Awesome

Jiacheng Chen , Ruizhi Deng , Yasutaka Furukawa

Simon Fraser University

ArXiv, Project page

This repository provides the official implementation of the paper PolyDiffuse: Polygonal Shape Reconstruction via Guided Set Diffusion Models. This branch contains the code of the floorplan reconstruction task. The code of the HD mapping task will be ready in a separate branch.

The implementation of PolyDiffuse refers to the following open-source works: EDM, RoomFormer, MapTR, HEAT. We thank the authors for releasing their source code.

Introduction video

https://github.com/woodfrog/poly-diffuse/assets/13405255/6dcd4bb6-9bd2-4fc8-aa65-c32b6ae0e92b

Catalog

1. Floorplan reconstruction

• PolyDiffuse code and model checkpoints (in this main branch)
• code and models of the adapted RoomFormer, which serves as our proposal generator (in the folder ./roomformer_adapted)

2. HD mapping

• PolyDiffuse code and model checkpoints (in the hd-mapping branch)

Preparation

Please follow the instructions below to install the environment as well as downloading the data and pretrained models.

Installation

First, install the environment via conda:

conda env create -f environment.yml -n polydiffuse
conda activate polydiffuse

Then install the Deformable attention module used in the denoising network:

cd src/models/polygon_models/ops
sh make.sh
cd ../../../..

Data

Download the data file from this link, put it into ./data and unzip.

Unzip the results of RoomFormer, which serves as one of our proposal generators:

cd init_results
unzip npy_roomformer_s3d_256.zip
cd ..

Model checkpoints

Download the pretrained models from this link, put it into ./training-runs and unzip.

The final file structure should be as follows:

data
  ├── processed_s3d # the preprocessed structured3d dataset
          │── train
          │── test
          └── val   
init_results
  ├── npy_roomformer_s3d_256  # RoomFormer results of the test scenes in npy format

training-runs
  ├── s3d_pretrained_ckpts  # pretrained checkpoints
          │── guide  # checkpoints of guidance network
          └── denoise  # checkpoints of denoising network

Testing

We first introduce the testing pipeline, which consists of sampling, quantitative evaluation, and qualitative visualization.

Sampling

First, run the sampling for all the test examples by:

CUDA_VISIBLE_DEVICES=0 bash scripts/sample.sh

Set up the --proposal_type argument to init with mimic rough annotations. Set --viz_results=True to show the visualization results including per-step GIF animation. Note that the default parameters in the above testing script assume the use of pretrained checkpoints. If you re-train the models, remember to set up the parameters accordingly.

Evaluation

Evaluate the results via:

python eval/evaluate_solution.py --dataset_path ./data/processed_s3d --dataset_type s3d --scene_id val --results_dir outputs/s3d_polydiffuse_step10/npy

The argument results_dir should be set up properly, which points to the sampling outputs.

This evaluation pipeline follows MonteFloor, HEAT, and RoomFormer.

Visualization

Get the qualitative visualization results used in the paper with the script eval/visualize_npy.py, including the GIF animation of the denoising process. Set up the paths in the script first, and then simply run:

python eval/visualize_npy.py

Training

The training of PolyDiffuse consists of two separate stages: 1) guidance training and 2) denoising training.

Guidance training

Train the guidance network by:

bash scripts/train_guide.sh

The training of the guidance network is very fast as there is no image information involved, and usually takes less than an hour.

Denoising training

Then train the denoising network by:

bash scripts/train.sh

Note that the path to the guidance network trained in the first stage needs to be set up properly with the argument guide_ckpt. The training takes around 30 hours with 4 NVIDIA RTX A5000 GPUs on our machine.

Adapted RoomFormer as the proposal generator

The folder ./roomformer_adapted contains our modified version of RoomFormer (see details in the paper). The same environment as PolyDiffuse works for this codebase, please check the folder README for pretrained weights and training/testing instructions.

Acknowledgements

This research is partially supported by NSERC Discovery Grants with Accelerator Supplements and DND/NSERC Discovery Grant Supplement, NSERC Alliance Grants, and John R. Evans Leaders Fund (JELF). We thank the Digital Research Alliance of Canada and BC DRI Group for providing computational resources.

Citation

If you find PolyDiffuse is helpful in your work, please consider starring 🌟 the repo and citing it by:

@article{Chen2023PolyDiffuse,
  title={PolyDiffuse: Polygonal Shape Reconstruction via Guided Set Diffusion Models},
  author={Jiacheng Chen and Ruizhi Deng and Yasutaka Furukawa},
  journal={ArXiv},
  year={2023},
  volume={abs/2306.01461}
}