Interactive 3D Object Detection with Prompts

Ruifei Zhang, Xiangru Lin, Wei Zhang, Jincheng Lu, Xuekuan Wang, Xiao Tan, Yingying Li, Errui Ding, Jingdong Wang, Guanbin Li* ;

"The evolution of 3D object detection hinges not only on advanced models but also on effective and efficient annotation strategies. Despite this progress, the labor-intensive nature of 3D object annotation remains a bottleneck, hindering further development in the field. This paper introduces a novel approach, incorporated with “prompt in 2D, detect in 3D” and “detect in 3D, refine in 3D” strategies, to 3D object annotation: multi-modal interactive 3D object detection. Firstly, by allowing users to engage with simpler 2D interaction prompts (e.g., clicks or boxes on a camera image or a bird’s eye view), we bridge the complexity gap between 2D and 3D spaces, reimagining the annotation workflow. Besides, Our framework also supports flexible iterative refinement to the initial 3D annotations, further assisting annotators in achieving satisfying results. Evaluation on the nuScenes dataset demonstrates the effectiveness of our method. And thanks to the prompt-driven and interactive designs, our approach also exhibits outstanding performance in open-set scenarios. This work not only offers a potential solution to the 3D object annotation problem but also paves the way for further innovations in the 3D object detection community."