URINet:通过语义和结构推理进行无监督点云旋转不变表示学习

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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引用次数: 0

摘要

近年来,人们提出了许多旋转不变网络,以减轻点云任意旋转造成的干扰。这些网络展示了强大的表征学习能力。然而,这些方法大多依赖于昂贵的人工标注监督来训练模型。此外,这些方法无法推理结构关系,也会丢失全局信息。为了解决这些问题,我们提出了一种无监督方法,无需人工标注即可实现全面的旋转不变表示。具体来说,我们提出了一种名为 URINet 的新型编码器-解码器架构,它通过结合局部语义信息和全局结构信息来学习点云表示,然后在没有旋转扰动的情况下重建输入。具体来说,编码器是一个双分支网络,其中基于图卷积的结构分支对局部区域之间的关系进行建模,以学习全局结构知识,而语义分支则学习旋转不变的局部语义特征。两个分支获得互补信息,全面探索点云。此外,为了避免不确定姿态带来的自我重建模糊性,还提出了一种双向配准方法,用于衡量无方向知识情况下的重建结果质量。对下游任务的广泛实验表明,所提出的方法在合成数据集和真实世界数据集上都大大超过了现有的最先进方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
URINet: Unsupervised point cloud rotation invariant representation learning via semantic and structural reasoning

In recent years, many rotation-invariant networks have been proposed to alleviate the interference caused by point cloud arbitrary rotations. These networks have demonstrated powerful representation learning capabilities. However, most of those methods rely on costly manually annotated supervision for model training. Moreover, they fail to reason the structural relations and lose global information. To address these issues, we present an unsupervised method for achieving comprehensive rotation invariant representations without human annotation. Specifically, we propose a novel encoder–decoder architecture named URINet, which learns a point cloud representation by combining local semantic and global structural information, and then reconstructs the input without rotation perturbation. In detail, the encoder is a two-branch network where the graph convolution based structural branch models the relationships among local regions to learn global structural knowledge and the semantic branch learns rotation invariant local semantic features. The two branches derive complementary information and explore the point clouds comprehensively. Furthermore, to avoid the self-reconstruction ambiguity brought by uncertain poses, a bidirectional alignment is proposed to measure the quality of reconstruction results without orientation knowledge. Extensive experiments on downstream tasks show that the proposed method significantly surpasses existing state-of-the-art methods on both synthetic and real-world datasets.

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来源期刊
Computer Vision and Image Understanding
Computer Vision and Image Understanding 工程技术-工程:电子与电气
CiteScore
7.80
自引率
4.40%
发文量
112
审稿时长
79 days
期刊介绍: The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views. Research Areas Include: • Theory • Early vision • Data structures and representations • Shape • Range • Motion • Matching and recognition • Architecture and languages • Vision systems
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