Object proposals estimation in depth image using compact 3D shape manifolds

Shuai Zheng*, Victor Adrian Prisacariu, Melinos Averkiou, Ming Ming Cheng, Niloy J. Mitra, Jamie Shotton, Philip H.S. Torr, Carsten Rother

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations


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 languageEnglish (US)
Title of host publicationPattern Recognition - 37th German Conference, GCPR 2015, Proceedings
EditorsBastian Leibe, Juergen Gall, Peter Gehler
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783319249469
StatePublished - 2015
Event37th German Conference on Pattern Recognition, GCPR 2015 - Aachen, Germany
Duration: Oct 7 2015Oct 10 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other37th German Conference on Pattern Recognition, GCPR 2015

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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