Yuru Peng

GARD: A Geometry-Informed and Uncertainty-Aware Baseline Method for Zero-Shot Roadside Monocular Object Detection

Roadside camera-based perception methods are in high demand for developing efficient vehicle-infrastructure collaborative perception systems. By focusing on object-level depth prediction, we explore the potential benefits of integrating environmental priors into such systems and propose a geometry-b