Towards Part-Based Understanding of RGB-D Scans

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Deep Learningpart-based-scan-understanding
Overview

Towards Part-Based Understanding of RGB-D Scans (CVPR 2021)

We propose the task of part-based scene understanding of real-world 3D environments: from an RGB-D scan of a scene, we detect objects, and for each object predict its decomposition into geometric part masks, which composed together form the complete geometry of the observed object.

Part-Based Scene Understanding

Download Paper (.pdf)

Demo samples

Part-Based Scene Understanding

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The core of this repository is a network, which takes as input preprocessed scan voxel crops and produces voxelized part trees. However, data preparation is very massive step before launching actual training and inference. That's why we release already prepared data for training and checkpoint to perform inference. If you want to launch training with our data, please follow the steps below:

  1. Clone repo: git clone https://github.com/alexeybokhovkin/part-based-scan-understanding.git

  2. Download data and/or checkpoint:
    ScanNet MLCVNet crops (finetune) [894M]
    ScanNet clean crops (pretraining) [995M]
    PartNet GT trees [103M]
    Parts priors [169M]
    Checkpoint [19M]

  3. For training, prepare augmented version of ScanNet crops with script dataproc/prepare_rot_aug_data.py. After this, create a folder with all necessary dataset metadata using script dataproc/gather_all_shapes.py

  4. Create config file similar to configs/config_gnn_scannet_allshapes.yaml (you need to provide paths to some directories and files)

  5. Launch training with train_gnn_scannet.py

Citation

If you use this framework please cite:

@article{Bokhovkin2020TowardsPU,
  title={Towards Part-Based Understanding of RGB-D Scans},
  author={Alexey Bokhovkin and V. Ishimtsev and Emil Bogomolov and D. Zorin and A. Artemov and Evgeny Burnaev and Angela Dai},
  journal={ArXiv},
  year={2020},
  volume={abs/2012.02094}
}
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Comments
  • scannet_shape_ids files and part segmentation

    scannet_shape_ids files and part segmentation

    First of all, thanks for the great work! I have two questions about this repo and your paper:

    1. It seems that txt files for scannet_shape_ids are required for prepare_rot_aug_data.py. But I cannot find them in the provided dataset files.
    2. Could you explain more details about part segmentation on 3D scans? I'm confused if the part segmentation labels for 3d scans are generated by 1) aligning PartNet data, 2) assigning part labels to overlapped regions. Do you provide point-wise (or voxel-wise) part segmentation annotation?
    opened by jeonghyunkeem 0
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