Are Dense Labels Always Necessary for 3D Object Detection from Point Cloud?
arxiv(2024)
摘要
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
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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