5 papers
Depth Anything in : Towards Scale Invariance in the Wild
Hualie Jiang, Ziyang Song, Zhiqiang Lou +2
Panoramic depth estimation provides a comprehensive solution for capturing complete environmental structural information, offering significant benefits for robotics and…
Self-Supervised Enhancement for Depth from a Lightweight ToF Sensor with Monocular Images
Laiyan Ding, Hualie Jiang, Jiwei Chen +1
Depth map enhancement using paired high-resolution RGB images offers a cost-effective solution for improving low-resolution depth data from lightweight ToF sensors. Nevertheless, n…
DEFOM-Stereo: Depth Foundation Model Based Stereo Matching
Hualie Jiang, Zhiqiang Lou, Laiyan Ding +4
Stereo matching is a key technique for metric depth estimation in computer vision and robotics. Real-world challenges like occlusion and non-texture hinder accurate disparity estim…
CFPNet: Improving Lightweight ToF Depth Completion via Cross-zone Feature Propagation
Laiyan Ding, Hualie Jiang, Rui Xu +1
Depth completion using lightweight time-of-flight (ToF) depth sensors is attractive due to their low cost. However, lightweight ToF sensors usually have a limited field of view (FO…
Towards Cross-View-Consistent Self-Supervised Surround Depth Estimation
Laiyan Ding, Hualie Jiang, Jie Li +2
Depth estimation is a cornerstone for autonomous driving, yet acquiring per-pixel depth ground truth for supervised learning is challenging. Self-Supervised Surround Depth Estimati…