Using priors for improving generalization inNon-Rigid structure-from-motion
Research output: Contribution to conference › Paper › Research › peer-review
This paper describes how the generalization ability of methods for non-rigid Structure-from-Motion can be improved by using priors. Most point tracks are often visible only in some of the images; predicting the missing data can be important. Previous Maximum-Likelihood (ML)-approaches on implicit non-rigid Structure-from-Motion generalize badly. Although the estimated model fits well to the visible training data, it often predicts the missing data badly. To improve generalization we propose to add a temporal smoothness prior and a continuous surface shape prior to an ML-approach. The temporal smoothness prior constrains the camera trajectory and the configuration weights to behave smoothly. The surface shape prior constrains consistently close image point tracks to have a similar implicit structure. We propose an algorithm for achieving a Maximum A Posteriori (MAP)-solution and show experimentally that the MAP-solution generalizes far better than the MLsolution. The proposed method is fully automatic: it handles a substantial amount of missing data as well as outlier contaminated data, and automatically estimates the rank of the measurement matrix.
Original language | English |
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Publication date | 2007 |
DOIs | |
Publication status | Published - 2007 |
Event | 2007 18th British Machine Vision Conference, BMVC 2007 - Warwick, United Kingdom Duration: 10 Sep 2007 → 13 Sep 2007 |
Conference
Conference | 2007 18th British Machine Vision Conference, BMVC 2007 |
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Country | United Kingdom |
City | Warwick |
Period | 10/09/2007 → 13/09/2007 |
ID: 392568462