2 papers
cs.CV2024
MAL: Motion-Aware Loss with Temporal and Distillation Hints for Self-Supervised Depth Estimation
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 lev…
cs.CV2024
PPEA-Depth: Progressive Parameter-Efficient Adaptation for Self-Supervised Monocular Depth Estimation
Yue-Jiang Dong, Yuan-Chen Guo, Ying-Tian Liu +2
Self-supervised monocular depth estimation is of significant importance with applications spanning across autonomous driving and robotics. However, the reliance on self-supervision…