MSMD-Net: Deep Stereo Matching with Multi-scale and Multi-dimension Cost Volume

23 Jun 2020Zhelun ShenYuchao DaiZhibo Rao

Deep end-to-end learning based stereo matching methods have achieved great success as witnessed by the leaderboards across different benchmarking datasets (KITTI, Middlebury, ETH3D, etc), where the cost volume representation is an indispensable step to the success. However, most existing work only employs a single cost volume, which cannot fully exploit the multi-scale cues in stereo matching and provide guidance for disparity refinement... (read more)

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