Are Dense Labels Always Necessary for 3D Object Detection from Point Cloud?

• URL: http://arxiv.org/abs/2403.02818v1
• Date: Tue, 5 Mar 2024 09:38:11 GMT
• Title: Are Dense Labels Always Necessary for 3D Object Detection from Point Cloud?
• Authors: Chenqiang Gao, Chuandong Liu, Jun Shu, Fangcen Liu, Jiang Liu, Luyu Yang, Xinbo Gao, and Deyu Meng
• Abstract summary: Current state-of-the-art (SOTA) 3D object detection methods often require a large amount of 3D bounding box annotations for training. We propose a novel sparsely-annotated framework, in which we just annotate one 3D object per scene. We develop the SS3D++ method that alternatively improves 3D detector training and confident fully-annotated scene generation.
• Score: 72.40353149833109
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current state-of-the-art (SOTA) 3D object detection methods often require a large amount of 3D bounding box annotations for training. However, collecting such large-scale densely-supervised datasets is notoriously costly. To reduce the cumbersome data annotation process, we propose a novel sparsely-annotated framework, in which we just annotate one 3D object per scene. Such a sparse annotation strategy could significantly reduce the heavy annotation burden, while inexact and incomplete sparse supervision may severely deteriorate the detection performance. To address this issue, we develop the SS3D++ method that alternatively improves 3D detector training and confident fully-annotated scene generation in a unified learning scheme. Using sparse annotations as seeds, we progressively generate confident fully-annotated scenes based on designing a missing-annotated instance mining module and reliable background mining module. Our proposed method produces competitive results when compared with SOTA weakly-supervised methods using the same or even more annotation costs. Besides, compared with SOTA fully-supervised methods, we achieve on-par or even better performance on the KITTI dataset with about 5x less annotation cost, and 90% of their performance on the Waymo dataset with about 15x less annotation cost. The additional unlabeled training scenes could further boost the performance. The code will be available at https://github.com/gaocq/SS3D2.

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