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SL3D: Self-supervised-Self-labeled 3D Recognition

2022-10-30 11:08:25
Fernando Julio Cendra, Lan Ma, Jiajun Shen, Xiaojuan Qi
arXiv_CV
                     
Abstract

There are a lot of promising results in 3D recognition, including classification, object detection, and semantic segmentation. However, many of these results rely on manually collecting densely annotated real-world 3D data, which is highly time-consuming and expensive to obtain, limiting the scalability of 3D recognition tasks. Thus in this paper, we study unsupervised 3D recognition and propose a Self-supervised-Self-Labeled 3D Recognition (SL3D) framework. SL3D simultaneously solves two coupled objectives, i.e., clustering and learning feature representation to generate pseudo labeled data for unsupervised 3D recognition. SL3D is a generic framework and can be applied to solve different 3D recognition tasks, including classification, object detection, and semantic segmentation. Extensive experiments demonstrate its effectiveness. Code is available at this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2210.16810

PDF

https://arxiv.org/pdf/2210.16810.pdf

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