Abstract

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 to employ 3D anchors to explicitly construct geometric 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, achieving state-of-the-art performance in 3D object detection and localization on the challenging KITTI dataset.

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