SymStereo: Stereo Matching using Induced Symmetry

Stereo Matching Methods for Imperfectly Rectified Stereo Images

Stereo matching has been under development for decades and is an important process for many applications. Difficulties in stereo matching include textureless regions, occlusion, illumination variation, the fattening effect, and discontinuity. These challenges are effectively solved in recently developed stereo matching algorithms. A new imperfect rectification problem has recently been encountered in stereo matching, and the problem results from the high resolution of stereo images. State-of-the-art stereo matching algorithms fail to exactly reconstruct the depth information using stereo images with imperfect rectification, as the imperfectly rectified image problems are not explicitly taken into account. In this paper, we solve the imperfect rectification problems, and propose matching stereo matching methods that based on absolute differences, square differences, normalized cross correlation, zero-mean normalized cross correlation, and rank and census transforms. Finally, we conduct experiments to evaluate these stereo matching methods using the Middlebury datasets. The experimental results show the proposed stereo matching methods can reduce error rate significantly for stereo images with imperfect rectification.

Piecewise-Planar StereoScan: Sequential Structure and Motion Using Plane Primitives

The article describes a pipeline that receives as input a sequence of stereo images, and outputs the camera motion and a Piecewise-Planar Reconstruction (PPR) of the scene. The pipeline, named Piecewise-Planar StereoScan (PPSS), works as follows: the planes in the scene are detected for each stereo view using semi-dense depth estimation; the relative pose is computed by a new closed-form minimal algorithm that only uses point correspondences whenever plane detections do not fully constrain the motion; the camera motion and the PPR are jointly refined by alternating between discrete optimization and continuous bundle adjustment; and, finally, the detected 3D planes are segmented in images using a new framework that handles low texture and visibility issues. PPSS is extensively validated in indoor and outdoor datasets, and benchmarked against two popular point-based SfM pipelines. The experiments confirm that plane-based visual odometry is resilient to situations of small image overlap, poor texture, specularity, and perceptual aliasing where the fast LIBVISO2 [1] pipeline fails. The comparison against VisualSfM+CMVS/PMVS [2] , [3] shows that, for a similar computational complexity, PPSS is more accurate and provides much more compelling and visually pleasant 3D models. These results strongly suggest that plane primitives are an advantageous alternative to point correspondences for applications of SfM and 3D reconstruction in man-made environments.