Song–Hai Zhang
Papers
6
Total Citations
67
H-Index
4
About
Song–Hai Zhang is a leading researcher in visual computing and autonomous perception, whose work bridges 3D scene understanding, place recognition, and self-supervised depth estimation. His most impactful contribution, TransLoc3D, introduces adaptive receptive fields for point-cloud-based large-scale place recognition—a critical capability for autonomous driving and robot navigation. This work has accumulated 48 combined citations, reflecting its influence on the field. Zhang has also advanced self-supervised monocular depth estimation with PPEA-Depth, a progressive parameter-efficient adaptation method that tackles the static-scene assumption in dynamic environments, and with MAL, a motion-aware loss framework incorporating temporal and distillation hints for multi-frame depth estimation. His research consistently addresses real-world challenges in mobile robotics, including multi-sensor fusion for 3D environment perception and reconstruction. By developing methods that work robustly in dynamic, unlabeled environments, Zhang is helping to make autonomous systems more reliable and data-efficient. His contributions are essential reading for anyone working in 3D vision, autonomous navigation, or self-supervised learning for robotics.
Research Focus
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