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BUOL: A Bottom-Up Framework with Occupancy-aware Lifting for Panoptic 3D Scene Reconstruction From A Single Image

Introduction

This is an official release of the paper BUOL: A Bottom-Up Framework with Occupancy-aware Lifting for Panoptic 3D Scene Reconstruction From A Single Image

BUOL: A Bottom-Up Framework with Occupancy-aware Lifting for Panoptic 3D Scene Reconstruction From A Single Image
Tao Chu, Pan Zhang, Qiong Liu, Jiaqi Wang
The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR 2023)

Results

The results of BUOL on each dataset are shown below. We have released the models.

Installation

Creat environment.

conda create -n buol -y
conda activate buol
conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch -c conda-forge -y

Install MinkowskiEngine.

git clone https://github.com/NVIDIA/MinkowskiEngine.git
cd MinkowskiEngine
python setup.py install --blas_include_dirs=${CONDA_PREFIX}/include --blas=openblas --force_cuda

Install PyMarchingCubes.

git clone https://github.com/xheon/PyMarchingCubes.git
cd PyMarchingCubes
git clone https://gitlab.com/libeigen/eigen.git
python setup.py install

Install other dependency packages.

pip install yacs fvcore
pip install opencv-python
conda install -c conda-forge openexr-python -y
pip install pyexr
pip install matplotlib
pip install plyfile
pip install loguru
pip install scipy

Run

Demo

Download front3d.pth and put it at models/front3d.pth, and run:

python demo.py

Train

Download datasets and put them in datasets/<dataset_name> as the following structure, and then set GPUS (e.g. GPUS: (0, 1, 2, 3)) and MODEL.EVAL: False in the config file, and train with multi-GPU:

python -m torch.distributed.launch --nproc_per_node=4 main.py --cfg configs/front.yaml

Test

Download the model or train the model, and then set MODEL.WEIGHTS as the model path. Set GPUS: (0,) and MODEL.EVAL: True in the config file, and test with one GPU:

python main.py --cfg configs/front.yaml

Datasets

3D FRONT

The 3D FRONT is a synthetic indoor dataset. We process it the same as Dahnert et al. (Panoptic 3D Scene Reconstruction from a Single RGB Image). You can download or process it from there.

Structure

front3d/
    <scene_id>/            
        ├── rgb_<frame_id>.png                  # Color image: 320x240x3
        ├── depth_<frame_id>.exr                # Depth image: 320x240x1
        ├── segmap_<frame_id>.mapped.npz        # 2D Segmentation: 320x240x2, with 0: pre-mapped semantics, 1: instances
        ├── geometry_<frame_id>.npz             # 3D Geometry: 256x256x256x1, truncated, (unsigned) distance field at 3cm voxel resolution and 12 voxel truncation.
        ├── segmentation_<frame_id>.mapped.npz  # 3D Segmentation: 256x256x256x2, with 0: pre-mapped semantics & instances
        ├── weighting_<frame_id>.mapped.npz     # 3D Weighting mask: 256x256x256x1

Matterport3D

The Matterport3D is a real-world indoor datasets. We follow Dahnert et al. to preprocess this dataset. In addition, we generate depth and room mask by rendering 3D scenes instead of using the origin version.

Structure

matterport/
    <scene_id>/            
        ├── <image_id>_i<frame_id>.png                    # Color image: 320x240x3
        ├── <image_id>_segmap<frame_id>.mapped.npz        # 2D Segmentation: 320x240x2, with 0: pre-mapped semantics, 1: instances
        ├── <image_id>_intrinsics_<camera_id>.png         # Intrinsics matrix: 4x4
        ├── <image_id>_geometry<frame_id>.npz             # 3D Geometry: 256x256x256x1, truncated, (unsigned) distance field at 3cm voxel resolution and 12 voxel truncation.
        ├── <image_id>_segmentation<frame_id>.mapped.npz  # 3D Segmentation: 256x256x256x2, with 0: pre-mapped semantics & instances
        ├── <image_id>_weighting<frame_id>.npz            # 3D Weighting mask: 256x256x256x1
matterport_depth_gen/
    <scene_id>/     
        ├── <posithion_id>_d<frame_id>.png                # Depth image: 320x240x1
matterport_room_mask/
    <scene_id>/   
        ├── <posithion_id>_rm<frame_id>.png               # room mask: 320x240x1

Citation

@inproceedings{chu2023buol,
  title={BUOL: A Bottom-Up Framework With Occupancy-Aware Lifting for Panoptic 3D Scene Reconstruction From a Single Image},
  author={Chu, Tao and Zhang, Pan and Liu, Qiong and Wang, Jiaqi},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={4937--4946},
  year={2023}
}