A Comparative Evaluation of 3D Geometries of Scenes Estimated using Factor Graph Based Disparity Estimation Algorithms
Abstract
Passive stereo vision systems are useful for estimating 3D geometries from digital images similar to the human biological system. In general, two cameras are situated at a known distance from each other and simultaneously capture images of the same scene from different views. This paper presents a comparative evaluation of 3D geometries of scenes estimated by three disparity estimation algorithms, namely the hybrid stereo matching algorithm (HCS), factor graph-based stereo matching algorithm (FGS), and a multi-resolution FGS algorithm (MR-FGS). Comparative studies were conducted using our stereo imaging system as well as hand-held, consumer-market digital cameras and camera phones of a variety of makes/models. Based on our experimental results, the factor graph algorithm (FGS) and multi-resolution factor graph algorithm (MR-FGS) result in a higher level of 3D reconstruction accuracy than the HCS algorithm. When compared with the FGS algorithm, MR-FGS provides a significant improvement in the disparity contrast along the depth boundaries and minimal depth discontinuities.
Publication Title
IS and T International Symposium on Electronic Imaging Science and Technology
Recommended Citation
Shabanian, H., & Balasubramanian, M. (2023). A Comparative Evaluation of 3D Geometries of Scenes Estimated using Factor Graph Based Disparity Estimation Algorithms. IS and T International Symposium on Electronic Imaging Science and Technology, 35 (17) https://doi.org/10.2352/EI.2023.35.17.3DIA-107
