Abstract

Acquiring 3D geometry of an object is a tedious and time-consuming task, typically requiring scanning the surface from multiple viewpoints. In this work we focus on reconstructing complete geometry from a single scan acquired with a low-quality consumer-level scanning device. Our method uses a collection of example 3D shapes to build structural part-based priors that are necessary to complete the shape. In our representation, we associate a local coordinate system to each part and learn the distribution of positions and orientations of all the other parts from the database, which implicitly also defines positions of symmetry planes and symmetry axes. At the inference stage, this knowledge enables us to analyze incomplete point clouds with substantial occlusions, because observing only a few regions is still sufficient to infer the global structure. Once the parts and the symmetries are estimated, both data sources, symmetry and database, are fused to complete the point cloud. We evaluate our technique on a synthetic dataset containing 481 shapes, and on real scans acquired with a Kinect scanner. Our method demonstrates high accuracy for the estimated part structure and detected symmetries, enabling higher quality shape completions in comparison to alternative techniques.

Minhyuk Sung, Vladimir G. Kim, Roland Angst, and Leonidas Guibas
Data-driven Structural Priors for Shape Completion
ACM Transactions on Graphics (Proc. SIGGRAPH Asia 2015)
Paper | Supplementary material | Slides | Code (GitHub)

Bibtex

@article{Sung:2015,
  author = {Sung, Minhyuk and Kim, Vladimir G. and Angst, Roland and Guibas, Leonidas},
  title = {Data-driven Structural Priors for Shape Completion},
  journal = {ACM Trans. Graph.},
  issue_date = {November 2015},
  volume = {34},
  number = {6},
  month = oct,
  year = {2015},
  url = {http://doi.acm.org/10.1145/2816795.2818094},
  doi = {10.1145/2816795.2818094},
}

Data Download

Ground truth datasets (41.8MB)
Benchmark results (2.2GB)
Kinect scan data (27.7MB)

If you use the data above, please also cite:
[Shen et al.2012] Chao-Hui Shen, Hongbo Fu, Kang Chen, and Shi-Min Hu
Structure Recovery by Part Assembly
ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2012)
[COSEG] Oana Sidi, Oliver van Kaick, Yanir Kleiman, Hao Zhang, Daniel Cohen-Or
Unsupervised Co-Segmentation of a Set of Shapes via Descriptor-Space Spectral Clustering
ACM Transactions on Graphics (Proc. SIGGRAPH Asia 2011)
[ShapeNet] http://shapenet.cs.stanford.edu/

Acknowledgements

This project was supported by NSF grants CCF 1011228, DMS 1228304, AFOSR grant FA9550-12-1-0372, ONR MURI grant N00014-13-1-0341, a Google Focused Research Award, the Korea Foundation and Advanced Studies, and the Max Planck Center for Visual Computing and Communications.