Lifeng An — accepted AI-conference papers

2023

Exploring the Mutual Influence Between Self-Supervised Single-Frame and Multi-Frame Depth Estimation

Jie Xiang, Yun Wang, Lifeng An, Haiyang Liu, Jian Liu

RA-L 2023

Although both self-supervised single-frame and multi-frame depth estimation methods only require unlabeled monocular videos for training, the information they leverage varies because single-frame methods mainly rely on appearance-based features while multi-frame methods focus on geometric cues. Cons

Cited by 8 Source ↗code ↗Scholar ↗

2022

Visual Attention-Based Self-Supervised Absolute Depth Estimation Using Geometric Priors in Autonomous Driving

Jie Xiang, Yun Wang, Lifeng An, Haiyang Liu, Zijun Wang, Jian Liu

RA-L 2022

Although existing monocular depth estimation methods have made great progress, predicting an accurate absolute depth map from a single image is still challenging due to the limited modeling capacity of networks and the scale ambiguity issue. In this paper, we introduce a fully Visual Attention-based

Cited by 27 Source ↗Scholar ↗