Abstract
Man-made objects, such as chairs, often have very large shape variations, making it challenging to detect them. In this work we investigate the task of finding particular object shapes from a single depth image. We tackle this task by exploiting the inherently low dimensionality in the object shape variations, which we discover and encode as a compact shape space. Starting from any collection of 3D models, we first train a low dimensional Gaussian Process Latent Variable Shape Space. We then sample this space, effectively producing infinite amounts of shape variations, which are used for training. Additionally, to support fast and accurate inference, we improve the standard 3D object category proposal generation pipeline by applying a shallow convolutional neural network-based filtering stage. This combination leads to considerable improvements for proposal generation, in both speed and accuracy. We compare our full system to previous state-of-the-art approaches, on four different shape classes, and show a clear improvement.
Original language | English (US) |
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Title of host publication | Pattern Recognition - 37th German Conference, GCPR 2015, Proceedings |
Editors | Bastian Leibe, Juergen Gall, Peter Gehler |
Publisher | Springer Verlag |
Pages | 196-208 |
Number of pages | 13 |
ISBN (Print) | 9783319249469 |
DOIs | |
State | Published - 2015 |
Event | 37th German Conference on Pattern Recognition, GCPR 2015 - Aachen, Germany Duration: Oct 7 2015 → Oct 10 2015 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 9358 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Other
Other | 37th German Conference on Pattern Recognition, GCPR 2015 |
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Country/Territory | Germany |
City | Aachen |
Period | 10/7/15 → 10/10/15 |
Bibliographical note
Publisher Copyright:© Springer International Publishing Switzerland 2015.
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science