4.6 Article

Two-Stream Based Multi-Stage Hybrid Decoder for Self-Supervised Multi-Frame Monocular Depth

期刊

IEEE ROBOTICS AND AUTOMATION LETTERS
卷 7, 期 4, 页码 12291-12298

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2022.3214787

关键词

Deep learning for visual perception; deep learning methods; visual learning

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资金

  1. Research Project of ZJU-League Research and Development Center

向作者/读者索取更多资源

This letter proposes a two-stream based multi-stage hybrid decoder to combine single-image scene information and multi-frame matching information, resulting in more accurate depth estimation results.
Self-supervised depth estimation has attracted a lot of attention recently due to its low cost. Despite using the self-supervision from image sequences, the current single-image based methods only infer depth from the scene information ignoring the matching information which is also important. Nevertheless, the matching information is not always reliable, especially in the texture-less and occlusion regions. Thus it would be attractive to combine the strength of single-image scene information and multi-frame matching information. In this letter, we propose a two-stream based multi-stage hybrid decoder to effectively accomplish the integration procedure. The hybrid decoder consists of two pathways for these two kinds of information respectively, and interactively fuses them. Specifically, a cost volume is built based on the scene prior to represent the matching information, and feeds back to the single-image pathway to complete the integration. To further facilitate the interactive integration, a multi-stage fusion strategy is embedded seamlessly into the hybrid decoder, resulting in more accurate depth results. Our approach outperforms the existing self-supervised methods on the KITTI and Cityscapes datasets.

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