Fundamental Matrix Computing Based on 3D Metrical Distance

• Published in: Algorithms Vol. 14; no. 3; p. 89
• Main Authors: Li, Xinsheng, Yuan, Xuedong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-03-2021
• Subjects: Algorithms; Cameras; computer vision; Criteria; fundamental matrix; Geometry; Image reconstruction; Methods; Metric space; metrical distance; Neural networks; Optimization; RANSAC; structure from motion
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a14030089

To reconstruct point geometry from multiple images, computation of the fundamental matrix is always necessary. With a new optimization criterion, i.e., the re-projective 3D metric geometric distance rather than projective space under RANSAC (Random Sample And Consensus) framework, our method can reveal the quality of the fundamental matrix visually through 3D reconstruction. The geometric distance is the projection error of 3D points to the corresponding image pixel coordinates in metric space. The reasonable visual figures of the reconstructed scenes are shown but only some numerical result were compared, as is standard practice. This criterion can lead to a better 3D reconstruction result especially in 3D metric space. Our experiments validate our new error criterion and the quality of fundamental matrix under the new criterion.