[CVPR'18] ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans
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README.md

ScanComplete

ScanComplete is a data-driven approach which takes an incomplete 3D scan of a scene as input and predicts a complete 3D model, along with per-voxel semantic labels. This work is based on our CVPR'18 paper, ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans.

Code

Installation:

Training is implemented with TensorFlow. This code is tested under TF1.3 and Python 2.7 on Ubuntu 16.04.

Training:

  • See run_train.sh for calling the training (will need to provide a path to the train data).
  • Trained models: models.zip

Testing:

  • See run_complete_scans_hierarchical.sh for testing on partial scans (needs paths to test data and model).

Data:

  • Test scenes as TF Records:
  • Train data as TF Records:
    • vox19_dim32.zip (12G) for training the 19cm hierarchy level
    • vox5-9-19_dim32.zip (240G) for training the 9cm and 5cm hierarchy levels. IMPORTANT: For training a hierarchy with more than just the finest level (e.g., 19-9-5 or 9-5 instead of just 5cm), the finer levels should be trained using the results from the trained model from the previous hierarchy level; i.e. this data will need to be edited.

Citation:

If you find our work useful in your research, please consider citing:

@inproceedings{dai2018scancomplete,
  title={ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans},
  author={Dai, Angela and Ritchie, Daniel and Bokeloh, Martin and Reed, Scott and Sturm, J{\"u}rgen and Nie{\ss}ner, Matthias},
  booktitle = {Proc. Computer Vision and Pattern Recognition (CVPR), IEEE},
  year = {2018}
}

Contact:

If you have any questions, please email Angela Dai at adai@cs.stanford.edu.