Abstract
Recovering structure and motion parameters given a image pair or a sequence of images is a well studied problem in computer vision. This is often achieved by employing Structure from Motion (SfM) or Simultaneous Localization and Mapping (SLAM) algorithms based on the real-time requirements. Recently, with the advent of Convolutional Neural Networks (CNNs) researchers have explored the possibility of using machine learning techniques to reconstruct the 3D structure of a scene and jointly predict the camera pose. In this work, we present a framework that achieves state-of-the-art performance on single image depth prediction for both indoor and outdoor scenes. The depth prediction system is then extended to predict optical flow and ultimately the camera pose and trained end-to-end. Our framework outperforms previous deep-learning based motion prediction approaches, and we also demonstrate that the state-of-the-art metric depths can be further improved using the knowledge of pose.
Original language | English |
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Title of host publication | Computer Vision – ACCV 2018 |
Subtitle of host publication | 14th Asian Conference on Computer Vision Perth, Australia, December 2–6, 2018 Revised Selected Papers, Part I |
Editors | C.V. Jawahar, Hongdong Li, Greg Mori, Konrad Schindler |
Place of Publication | Cham Switzerland |
Publisher | Springer |
Pages | 625-642 |
Number of pages | 18 |
ISBN (Electronic) | 9783030208875 |
ISBN (Print) | 9783030208868 |
DOIs | |
Publication status | Published - 2019 |
Event | Asian Conference on Computer Vision 2018 - Perth, Australia Duration: 2 Dec 2018 → 6 Dec 2018 Conference number: 14th http://accv2018.net/ https://link.springer.com/book/10.1007/978-3-030-20887-5 (Proceedings) |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 11361 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | Asian Conference on Computer Vision 2018 |
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Abbreviated title | ACCV 2018 |
Country | Australia |
City | Perth |
Period | 2/12/18 → 6/12/18 |
Internet address |
Keywords
- Depth
- Indoor and outdoor datasets
- Optical flow
- Pose prediction