Plane labeling trinocular stereo matching with baseline recovery

Abstract

In this paper we present an algorithm which recovers the rigid transformation that describes the displacement of a binocular stereo rig in a scene, and uses this to include a third image to perform dense trinocular stereo matching and reduce some of the ambiguities inherent to binocular stereo. The core idea of the proposed algorithm is the assumption that the binocular baseline is projected to the third view, and thus can be used to constrain the transformation estimation of the stereo rig. Our approach shows improved performance over binocular stereo, and the accuracy of the recovered motion allows to compute optical flow from a single disparity map. These claims are validated with the KITTI 2012 data set.

TrimBot2020: an outdoor robot for automatic gardening

“…For scene understanding and servoing of the robotic arm to the target bushes and roses, TrimBot2020 has developed precise algorithms for disparity computation from monocular images (DeMoN) [11] and from stereo images, based on convolutional neural networks (DispNet) [12], 3D plane labeling [13] and trinocular matching with baseline recovery [14]. An algorithm for optical flow estimation was also developed [15], that is based on a multi-stage CNN approach with itarative refinement of its own predictions.…”