Leveraging Single-View Images for Unsupervised 3D Point Cloud Completion

Wu, Lintai; Zhang, Qijian; Hou, Junhui; Xu, Yong

doi:10.48550/arxiv.2212.00564

Cited by 1 publication

(2 citation statements)

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“…Aiello et al [36] and Zhang et al [37] explore complementary information and exploit cross-modal data use in coarse-to-fine completion by using images as a weak supervision signal. Wu et al [38] use 2D feature information from images combined with 3D feature information from partial point clouds for an unsupervised completion. Supervised, weakly-supervised and unsupervised approaches are discussed in Section IV.…”

Section: A Inputsmentioning

confidence: 99%

“…The key points are used to generate a surface skeleton based on geometric priors which are then refined for the final output. Wu et al [38] use 2D images of the objects to extract 2D features and combine them with the 3D features extracted from the partial point clouds. ACL-SPC [104] uses a self-supervised adaptive control-loop framework that only uses a single partial input and no prior information.…”

Section: B Learning-based Approachmentioning

confidence: 99%

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Point Cloud Completion: A Survey

Tesema,

Hill,

Jones

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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Point cloud completion is the task of producing a complete 3D shape given an input of a partial point cloud. It has become a vital process in 3D computer graphics, vision and applications such as autonomous driving, robotics, and augmented reality. These applications often rely on the presence of a complete 3D representation of the environment. Over the past few years, many completion algorithms have been proposed and a substantial amount of research has been carried out. However, there are not many in-depth surveys that summarise the research progress in such a way that allows users to make an informed choice of what algorithms to employ given the type of data they have, the end result they want, the challenges they may face and the possible strategies they could use. In this study, we present a comprehensive survey and classification of papers on point cloud completion untill August 2023 based on the strategies, techniques, inputs, outputs, and network architectures. We will also cover datasets, evaluation methods, and application areas in point cloud completion. Finally, we discuss challenges faced by the research community and future research directions.

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Section: A Inputsmentioning

confidence: 99%

Section: B Learning-based Approachmentioning

confidence: 99%