Abstract
Heavily relying on 3D annotations limits the real-world application of 3D object detection. In this paper, we propose a method that does not demand any 3D annotation, while being able to predict full-oriented 3D bounding boxes. Our method, called Recursive Cross-View (RCV), transforms 3D detection into several 2D detection tasks, which only consume some 2D labels, based on the three-view principle. We propose a recursive paradigm, in which instance segmentation and 3D bounding box generation by Cross-View are implemented recursively until convergence. Specifically, a frustum is proposed via a 2D detector, followed by the recursive paradigm that finally outputs a full-oriented 3D box, class, and score. To justify that our method can be quickly used to new tasks in real-world scenarios, we do three experiments, namely indoor 3D human detection, full-oriented 3D hand detection, and real-time detection on a real 3D sensor. RCV achieves decent performance in these experiments. Once trained, our method can be viewed as a 3D annotation tool. Consequently, we formulate two 3D labeled dataset, namely '3D_HUMAN' and 'D_HAND', based on RCV, which could be used to pre-train other 3D detectors. Furthermore, estimated on the SUN RGB-D benchmark, our method achieves comparable performance with some full 3D supervised learning methods. RCV is the first 3D detection method that does not consume 3D labels and yields full-oriented 3D boxes on point clouds.
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URL
https://arxiv.org/abs/2211.07108
