5 papers
CoIn3D: Revisiting Configuration-Invariant Multi-Camera 3D Object Detection
Zhaonian Kuang, Rui Ding, Haotian Wang +3
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. Howe…
RayD3D: Distilling Depth Knowledge Along the Ray for Robust Multi-View 3D Object Detection
Rui Ding, Zhaonian Kuang, Zongwei Zhou +3
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 d…
Multi-Modal Decouple and Recouple Network for Robust 3D Object Detection
Rui Ding, Zhaonian Kuang, Yuzhe Ji +3
Multi-modal 3D object detection with bird's eye view (BEV) has achieved desired advances on benchmarks. Nonetheless, the accuracy may drop significantly in the real world due to da…
Selective Transfer Learning of Cross-Modality Distillation for Monocular 3D Object Detection
Rui Ding, Meng Yang, Nanning Zheng
Monocular 3D object detection is a promising yet ill-posed task for autonomous vehicles due to the lack of accurate depth information. Cross-modality knowledge distillation could e…
Object-Scene-Camera Decomposition and Recomposition for Data-Efficient Monocular 3D Object Detection
Zhaonian Kuang, Rui Ding, Meng Yang +2
Monocular 3D object detection (M3OD) is intrinsically ill-posed, hence training a high-performance deep learning based M3OD model requires a humongous amount of labeled data with c…