Learning 3D Shape Completion Under Weak Supervision

We address the problem of 3D shape completion from sparse and noisy point clouds, a fundamental problem in computer vision and robotics. Recent approaches are either data-driven or learning-based: Data-driven approaches rely on a shape model whose parameters are optimized to fit the observations; Le...

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Bibliographic Details
Published in:International journal of computer vision Vol. 128; no. 5; pp. 1162 - 1181
Main Authors: Stutz, David, Geiger, Andreas
Format: Journal Article
Language:English
Published: New York Springer US 01.05.2020
Springer
Springer Nature B.V
Subjects:
Artificial Intelligence
Artificial neural networks
Computer Imaging
Computer Science
Computer vision
Image Processing and Computer Vision
Image reconstruction
Machine vision
Object recognition
Pattern Recognition
Pattern Recognition and Graphics
Robotics
Shape optimization
Special Issue on Deep Learning for Robotic Vision
Supervised learning
Supervision
Three dimensional models
Vision
Benchmark
3D reconstruction
Weakly-supervised learning
3D shape completion
Amortized inference
ISSN:0920-5691, 1573-1405
Online Access:Get full text
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Summary:We address the problem of 3D shape completion from sparse and noisy point clouds, a fundamental problem in computer vision and robotics. Recent approaches are either data-driven or learning-based: Data-driven approaches rely on a shape model whose parameters are optimized to fit the observations; Learning-based approaches, in contrast, avoid the expensive optimization step by learning to directly predict complete shapes from incomplete observations in a fully-supervised setting. However, full supervision is often not available in practice. In this work, we propose a weakly-supervised learning-based approach to 3D shape completion which neither requires slow optimization nor direct supervision. While we also learn a shape prior on synthetic data, we amortize, i.e., learn , maximum likelihood fitting using deep neural networks resulting in efficient shape completion without sacrificing accuracy. On synthetic benchmarks based on ShapeNet (Chang et al. Shapenet: an information-rich 3d model repository, 2015 . arXiv:1512.03012 ) and ModelNet (Wu et al., in: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), 2015 ) as well as on real robotics data from KITTI (Geiger et al., in: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), 2012 ) and Kinect (Yang et al., 3d object dense reconstruction from a single depth view, 2018 . arXiv:1802.00411 ), we demonstrate that the proposed amortized maximum likelihood approach is able to compete with the fully supervised baseline of Dai et al. (in: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), 2017 ) and outperforms the data-driven approach of Engelmann et al. (in: Proceedings of the German conference on pattern recognition (GCPR), 2016 ), while requiring less supervision and being significantly faster.
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ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-018-1126-y