Accurate, Dense, and Robust Multi-View Stereopsis

: This paper proposes a novel algorithm for calibrated multi-view stereopsis that outputs a (quasi) dense set of rectangular patches covering the surfaces visible in the input images. This algorithm does not require any initialization in the form of a bounding volume, and it detects and discards automatically outliers and obstacles. It does not perform any smoothing across nearby features, yet is currently the top performer in terms of both coverage and accuracy for four of the six benchmark datasets presented in [20]. The keys to its performance are effective techniques for enforcing local photometric consistency and global visibility constraints. Stereopsis is implemented as a match, expand, and filter procedure, starting from a sparse set of matched keypoints, and repeatedly expanding these to nearby pixel correspondences before using visibility constraints to filter away false matches. A simple but effective method for turning the resulting patch model into a mesh appropriate for i...

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Yasutaka Furukawa, Jean Ponce
• Keywords: Benchmark Datasets, Computer Vision, CVPR 2007, Global Visibility Constraints, Local Photometric Consistency, Multi-view Stereopsis, Nearby Pixel Correspondences