ADU-Depth: Attention-based Distillation with Uncertainty Modeling for Depth Estimation

no code implementations • 26 Sep 2023 • Zizhang Wu, Zhuozheng Li, Zhi-Gang Fan, Yunzhe Wu, Xiaoquan Wang, Rui Tang, Jian Pu

Monocular depth estimation is challenging due to its inherent ambiguity and ill-posed nature, yet it is quite important to many applications.

Domain Adaptation Knowledge Distillation +2

Learning Monocular Depth in Dynamic Environment via Context-aware Temporal Attention

no code implementations • 12 May 2023 • Zizhang Wu, Zhuozheng Li, Zhi-Gang Fan, Yunzhe Wu, Yuanzhu Gan, Jian Pu, Xianzhi Li

During the refinement process, context-aware temporal attention (CTA) is developed to capture the global temporal-context correlations to maintain the feature consistency and estimation integrity of moving objects.

Autonomous Driving Monocular Depth Estimation +1

Surround-view Fisheye BEV-Perception for Valet Parking: Dataset, Baseline and Distortion-insensitive Multi-task Framework

no code implementations • 8 Dec 2022 • Zizhang Wu, Yuanzhu Gan, Xianzhi Li, Yunzhe Wu, Xiaoquan Wang, Tianhao Xu, Fan Wang

Most existing networks based on public datasets may generalize suboptimal results on these valet parking scenes, also affected by the fisheye distortion.

Autonomous Driving

Attention-Based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object Detection

no code implementations • 30 Nov 2022 • Zizhang Wu, Yunzhe Wu, Jian Pu, Xianzhi Li, Xiaoquan Wang

Specifically, we leverage intermediate features and responses for knowledge distillation.

Knowledge Distillation Monocular 3D Object Detection +3