Triangulation Learning Network: from Monocular to Stereo 3D Object Detection

CVPR 2019 Zengyi QinJinglu WangYan Lu

In this paper, we study the problem of 3D object detection from stereo images, in which the key challenge is how to effectively utilize stereo information. Different from previous methods using pixel-level depth maps, we propose employing 3D anchors to explicitly construct object-level correspondences between the regions of interest in stereo images, from which the deep neural network learns to detect and triangulate the targeted object in 3D space... (read more)

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