Zhaonian Kuang — accepted AI-conference papers

2026

CoIn3D: Revisiting Configuration-Invariant Multi-Camera 3D Object Detection

Zhaonian Kuang, Rui Ding, Haotian Wang, Xinhu Zheng, Meng Yang, Gang Hua

CVPR 2026

Multi-camera 3D object detection (MC3D) has attracted increasing attention with the growing deployment of multi-sensor physical agents, such as robots and autonomous vehicles. However, MC3D models still struggle to generalize to unseen platforms with new multi-camera configurations. Current solution

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2026

RayD3D: Distilling Depth Knowledge Along the Ray for Robust Multi-View 3D Object Detection

Rui Ding, Zhaonian Kuang, Zongwei Zhou, Meng Yang, Xinhu Zheng, Gang Hua

AAAI 2026technical

Multi-view 3D detection with bird’s eye view (BEV) is crucial for autonomous driving and robotics, but its robustness in real-world is limited as it struggles to predict accurate depth values. A mainstream solution, cross-modal distillation, transfers depth information from LiDAR to camera models bu

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