5 papers
The Midas Touch for Metric Depth: Interpretable Metricization for Generalizable Depth Perception
Mengtan Zhang, Yu Ma, Shiqi Jiang +6
Recent foundation models have drastically improved the generalization of monocular relative depth estimation, yet recovering accurate metric depth from their estimates for depth pe…
The Midas Touch for Metric Depth
Yu Ma, Zizhan Guo, Zuyi Xiong +5
Recent advances have markedly improved the cross-scene generalization of relative depth estimation, yet its practical applicability remains limited by the absence of metric scale,…
DST-Calib: A Dual-Path, Self-Supervised, Target-Free LiDAR-Camera Extrinsic Calibration Network
Zhiwei Huang, Yanwei Fu, Yi Zhou +3
LiDAR-camera extrinsic calibration is essential for multi-modal data fusion in robotic perception systems. However, existing approaches typically rely on handcrafted calibration ta…
Single-Frame Point-Pixel Registration via Supervised Cross-Modal Feature Matching
Yu Han, Zhiwei Huang, Yanting Zhang +5
Point-pixel registration between LiDAR point clouds and camera images is a fundamental yet challenging task in autonomous driving and robotic perception. A key difficulty lies in t…
Environment-Driven Online LiDAR-Camera Extrinsic Calibration
Zhiwei Huang, Jiaqi Li, Hongbo Zhao +5
LiDAR-camera extrinsic calibration (LCEC) is crucial for multi-modal data fusion in autonomous robotic systems. Existing methods, whether target-based or target-free, typically rel…