MAL: Motion-Aware Loss with Temporal and Distillation Hints for Self-Supervised Depth Estimation
Yue-Jiang Dong,Fang-Lue Zhang,Song-Hai Zhang,Yue-Jiang Dong,Fang-Lue Zhang,Song-Hai Zhang
Depth perception is crucial for a wide range of robotic applications. Multi-frame self-supervised depth estimation methods have gained research interest due to their ability to leverage large-scale, unlabeled real-world data. However, the self-supervised methods often rely on the assumption of a static scene and their performance tends to degrade in dynamic environments. To address this issue, we ...
