Accurate, Dense, and Robust Multiview Stereopsis
Google (United States) · Institut national de recherche en informatique et en automatique · +2 more institutions
Abstract
This paper proposes a novel algorithm for multiview stereopsis that outputs a dense set of small rectangular patches covering the surfaces visible in the images. Stereopsis is implemented as a match, expand, and filter procedure, starting from a sparse set of matched keypoints, and repeatedly expanding these before using visibility constraints to filter away false matches. The keys to the performance of the proposed algorithm are effective techniques for enforcing local photometric consistency and global visibility constraints. Simple but effective methods are also proposed to turn the resulting patch model into a mesh which can be further refined by an algorithm that enforces both photometric consistency and…
Citation impact
- FWCI
- 110.30
- Percentile
- 100%
- References
- 38
Authors
2Topics & keywords
- Artificial intelligence
- Computer vision
- Computer science
- Visibility
- Initialization
- Outlier
- Stereopsis
- Bounding overwatch
- Sustainable cities and communities