The Edge of Depth: Explicit Constraints between Segmentation and Depth
This work addresses a specific challenge in computer vision for applications like autonomous driving, but it is incremental as it builds on existing methods with a novel constraint.
The paper tackles the problem of improving unsupervised monocular depth estimation by explicitly enforcing border consistency with semantic segmentation, resulting in state-of-the-art performance on the KITTI dataset.
In this work we study the mutual benefits of two common computer vision tasks, self-supervised depth estimation and semantic segmentation from images. For example, to help unsupervised monocular depth estimation, constraints from semantic segmentation has been explored implicitly such as sharing and transforming features. In contrast, we propose to explicitly measure the border consistency between segmentation and depth and minimize it in a greedy manner by iteratively supervising the network towards a locally optimal solution. Partially this is motivated by our observation that semantic segmentation even trained with limited ground truth (200 images of KITTI) can offer more accurate border than that of any (monocular or stereo) image-based depth estimation. Through extensive experiments, our proposed approach advances the state of the art on unsupervised monocular depth estimation in the KITTI.