Abstract
Depth from triangulation has traditionally been investigated in a number of independent threads of research, with methods such as stereo, laser scanning, and coded structured light considered separately. In this paper, we propose a common framework called spacetime stereo that unifies and generalizes many of these previous methods. To show the practical utility of the framework, we develop two new algorithms for depth estimation: depth from unstructured illumination change and depth estimation in dynamic scenes. Based on our analysis, we show that methods derived from the spacetime stereo framework can be used to recover depth in situations in which existing methods perform poorly.
Original language | English (US) |
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Pages (from-to) | 296-302 |
Number of pages | 7 |
Journal | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Volume | 27 |
Issue number | 2 |
DOIs | |
State | Published - Feb 2005 |
All Science Journal Classification (ASJC) codes
- Software
- Artificial Intelligence
- Applied Mathematics
- Computer Vision and Pattern Recognition
- Computational Theory and Mathematics
Keywords
- Depth from triangulation
- Spacetime stereo
- Stereo