MGDepth: Motion-Guided Cost Volume For Self-Supervised Monocular Depth
  In Dynamic Scenarios
Kaichen Zhou Jia-Xing Zhong Jia-Wang Bian Qian Xie Jian-Qing Zheng Niki Trigoni Andrew Markham
Despite advancements in self-supervised monocular depth estimation,
challenges persist in dynamic scenarios due to the dependence on assumptions
about a static world. In this paper, we present MGDepth, a Motion-Guided Cost
Volume Depth Net, to achieve precise depth estimation for both dynamic objects
and static backgrounds, all while maintaining computational efficiency. To
tackle the challenges posed by dynamic content, we incorporate optical flow and
coarse monocular depth to create a novel static reference frame. This frame is
then utilized to build a motion-guided cost volume in collaboration with the
target frame. Additionally, to enhance the accuracy and resilience of the
network structure, we introduce an attention-based depth net architecture to
effectively integrate information from feature maps with varying resolutions.
Compared to methods with similar computational costs, MGDepth achieves a
significant reduction of approximately seven percent in root-mean-square error
for self-supervised monocular depth estimation on the KITTI-2015 dataset.