Two-Stream Based Multi-Stage Hybrid Decoder for Self-Supervised Multi-Frame Monocular Depth
Yangqi Long, Huimin Yu, Biyang Liu
Abstract
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.