Note: An updated and improved version of this approach is available as pre-print on ArXiv and the corresponding repository is davidstutz/arxiv2018-improved-shape-completion.
This repository contains paper and code corresponding to:
D. Stutz, A. Geiger. Learning 3D Shape Completion from Laser Scan Data with Weak Supervision. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
Please cite as:
@inproceedings{Stutz2018CVPR,
title = {Learning 3D Shape Completion from Laser Scan Data with Weak Supervision },
author = {Stutz, David and Geiger, Andreas},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
publisher = {IEEE Computer Society},
year = {2018}
}
Also check the project page for the final publication, code and data.
Here, you can find:
paper/: the LaTeX source of the final paper.code/:- davidstutz/daml-shape-completion, Torch and C++ implementation of the proposed approach and baselines as well as the created benchmarks.
- davidstutz/mesh-evaluation, C++ implementation of mesh-to-mesh / mesh-to-point distance used for evaluation.
- davidstutz/bpy-visualization-utils,
Python and Blender (
bpy) utilities for visualization as shown below. - davidstutz/mesh-voxelization, C++ implementation of mesh voxelization for computing occupancy grids and signed distance functions from watertight meshes.
Licenses for source code and data corresponding to:
D. Stutz, A. Geiger. Learning 3D Shape Completion from Laser Scan Data with Weak Supervision. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
Note that the source code and/or data is based on other projects for which separate licenses apply. See the corresponding subrepositories for details.
Copyright (c) 2018 David Stutz, Max-Planck-Gesellschaft
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The authors hereby grant you a non-exclusive, non-transferable, free of charge right to copy, modify, merge, publish, distribute, and sublicense the Software for the sole purpose of performing non-commercial scientific research, non-commercial education, or non-commercial artistic projects.
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Copyright (c) 2018 David Stutz, Max-Planck-Gesellschaft
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You agree to cite the corresponding papers (see above) in documents and papers that report on research using the Dataset.