Constrained Optimization for Plane-Based Stereo

Abstract

Depth and surface normal estimation are crucial components in understanding 3D scene geometry from calibrated stereo images. In this paper, we propose visibility and disparity magnitude constraints for slanted patches in the scene. These constraints can be used to associate geometrically feasible planes with each point in the disparity space. The new constraints are validated in the PatchMatch Stereo framework. We use these new constraints not only for initialization, but also in the local plane refinement step of this iterative algorithm. The proposed constraints increase the probability of estimating correct plane parameters, and lead to an improved 3D reconstruction of the scene. Furthermore, the proposed constrained initialization reduces the number of iterations before convergence to the optimal plane parameters. In addition, as most stereo image pairs are not perfectly rectified, we modify the view propagation process by assigning the plane parameters to the neighbors of the candidate pixel. To update the plane parameters in the plane refinement step, we use a gradient free non-linear optimizer. The benefits of the new initialization, propagation, and refinement schemes are demonstrated.

Stereo matching algorithm using census transform and segment tree for depth estimation

“…The constructed dataset comprises of stereo images shown with varied ambient illuminations and many exposures, with and without a mirror sphere of the lighting conditions. Among the proposed algorithms that used the Middlebury datasets as their benchmarks are optimization for plane-based stereo [12], memory-efficient and robust [13], adaptive cross guided filter with weights [14], and edge-based disparity map estimation [15].…”

A study of stereo matching algorithm on low texture and depth discontinuity regions

“…The combination can be include; multiple pixel matching techniques; multiple block matching techniques; multiple feature matching techniques and blending of any matching techniques. An example of blending matching technique was proposed by [10], where the matching cost stage was combined using speeded up robust features (SURF) matching [11] and CT [12]. Combining multiple matching techniques will increase the robustness of the algorithm, but the computational load and processing time will also increase as the number of technique combined increases.…”

“…The introduction of d y , the threshold, edges with larger weights than the threshold are removed from MST, forming the forest F of V, presented in (12),…”