Joint Self-supervised Depth and Optical Flow Estimation towards Dynamic Objects
This addresses a specific bottleneck in depth estimation for autonomous driving or robotics by improving accuracy in dynamic scenes, though it appears incremental as it builds on existing self-supervised methods.
The paper tackles the problem of dynamic objects causing uncertainty in inter-frame-supervised depth estimation by integrating optical flow information, resulting in a joint framework that outperforms existing depth estimators on the KITTI Depth dataset and shows competitive optical flow performance on KITTI Flow 2015.
Significant attention has been attracted to deep learning-based depth estimates. Dynamic objects become the most hard problems in inter-frame-supervised depth estimates due to the uncertainty in adjacent frames. Thus, integrating optical flow information with depth estimation is a feasible solution, as the optical flow is an essential motion representation. In this work, we construct a joint inter-frame-supervised depth and optical flow estimation framework, which predicts depths in various motions by minimizing pixel wrap errors in bilateral photometric re-projections and optical vectors. For motion segmentation, we adaptively segment the preliminary estimated optical flow map with large areas of connectivity. In self-supervised depth estimation, different motion regions are predicted independently and then composite into a complete depth. Further, the pose and depth estimations re-synthesize the optical flow maps, serving to compute reconstruction errors with the preliminary predictions. Our proposed joint depth and optical flow estimation outperforms existing depth estimators on the KITTI Depth dataset, both with and without Cityscapes pretraining. Additionally, our optical flow results demonstrate competitive performance on the KITTI Flow 2015 dataset.