Partition-Based Point Cloud Completion Network with Density Refinement

Li, Jianxin; Si, Guannan; Liang, Xinyu; An, Zhaoliang; Tian, Pengxin; Zhou, Fengyu

doi:10.3390/e25071018

Open AccessArticle

Partition-Based Point Cloud Completion Network with Density Refinement

by

Jianxin Li

¹,

Guannan Si

^1,*,

Xinyu Liang

¹,

Zhaoliang An

¹,

Pengxin Tian

¹ and

Fengyu Zhou

²

¹

School of Electrical Engineering, Academy of Information Sciences, Shandong Jiaotong University, Jinan 250357, China

²

School of Control Science and Engineering, Shandong University, Jinan 250012, China

^*

Author to whom correspondence should be addressed.

Entropy 2023, 25(7), 1018; https://doi.org/10.3390/e25071018

Submission received: 5 May 2023 / Revised: 10 June 2023 / Accepted: 25 June 2023 / Published: 2 July 2023

(This article belongs to the Special Issue Deep Learning Models and Applications to Computer Vision)

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Abstract

In this paper, we propose a novel method for point cloud complementation called PADPNet. Our approach uses a combination of global and local information to infer missing elements in the point cloud. We achieve this by dividing the input point cloud into uniform local regions, called perceptual fields, which are abstractly understood as special convolution kernels. The set of point clouds in each local region is represented as a feature vector and transformed into N uniform perceptual fields as the input to our transformer model. We also designed a geometric density-aware block to better exploit the inductive bias of the point cloud’s 3D geometric structure. Our method preserves sharp edges and detailed structures that are often lost in voxel-based or point-based approaches. Experimental results demonstrate that our approach outperforms other methods in reducing the ambiguity of output results. Our proposed method has important applications in 3D computer vision and can efficiently recover complete 3D object shapes from missing point clouds.

Keywords: convolutional neural networks; point cloud completion; gridding; radar; geometric density

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MDPI and ACS Style

Li, J.; Si, G.; Liang, X.; An, Z.; Tian, P.; Zhou, F. Partition-Based Point Cloud Completion Network with Density Refinement. Entropy 2023, 25, 1018. https://doi.org/10.3390/e25071018

AMA Style

Li J, Si G, Liang X, An Z, Tian P, Zhou F. Partition-Based Point Cloud Completion Network with Density Refinement. Entropy. 2023; 25(7):1018. https://doi.org/10.3390/e25071018

Chicago/Turabian Style

Li, Jianxin, Guannan Si, Xinyu Liang, Zhaoliang An, Pengxin Tian, and Fengyu Zhou. 2023. "Partition-Based Point Cloud Completion Network with Density Refinement" Entropy 25, no. 7: 1018. https://doi.org/10.3390/e25071018

APA Style

Li, J., Si, G., Liang, X., An, Z., Tian, P., & Zhou, F. (2023). Partition-Based Point Cloud Completion Network with Density Refinement. Entropy, 25(7), 1018. https://doi.org/10.3390/e25071018

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Partition-Based Point Cloud Completion Network with Density Refinement

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