Related papers: Stereo Object Matching Network

Stereo Object Matching Network

URL: http://arxiv.org/abs/2103.12498v1
Date: Tue, 23 Mar 2021 12:54:43 GMT
Title: Stereo Object Matching Network
Authors: Jaesung Choe, Kyungdon Joo, Francois Rameau, In So Kweon
Abstract summary: This paper presents a stereo object matching method that exploits both 2D contextual information from images and 3D object-level information. We present two novel strategies to handle 3D objectness in the cost volume space: selective sampling (RoISelect) and 2D-3D fusion.
Score: 78.35697025102334
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: This paper presents a stereo object matching method that exploits both 2D contextual information from images as well as 3D object-level information. Unlike existing stereo matching methods that exclusively focus on the pixel-level correspondence between stereo images within a volumetric space (i.e., cost volume), we exploit this volumetric structure in a different manner. The cost volume explicitly encompasses 3D information along its disparity axis, therefore it is a privileged structure that can encapsulate the 3D contextual information from objects. However, it is not straightforward since the disparity values map the 3D metric space in a non-linear fashion. Thus, we present two novel strategies to handle 3D objectness in the cost volume space: selective sampling (RoISelect) and 2D-3D fusion (fusion-by-occupancy), which allow us to seamlessly incorporate 3D object-level information and achieve accurate depth performance near the object boundary regions. Our depth estimation achieves competitive performance in the KITTI dataset and the Virtual-KITTI 2.0 dataset.

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