Paper
18 November 2024 PointVQDM: point cloud completion via vector quantized diffusion model
Ziyuan Lu, Qinglong Jiao, Liangdong Xu
Author Affiliations +
Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134032D (2024) https://doi.org/10.1117/12.3051321
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China
Abstract
Due to occlusion and other problems during point cloud scanning, the collected point clouds are often incomplete. Therefore, reconstructing a complete point cloud based on a partial point cloud with missing information is of great significance in practical work. In this paper, we propose a point cloud completion method (PointVQDM) based on the VQ-Diffusion model, which uses a discrete diffusion model to model in the latent space for shape reconstruction. Specifically, we obtain the corresponding vector combination based on the partial point cloud, and use the point cloud quantization network to decode it to obtain a complete point cloud. Experimental results on multiple datasets show that our PointVQDM outperforms the most advanced completion network. Moreover, thanks to our feature fusion method, we achieve diverse and high-quality generation results.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Ziyuan Lu, Qinglong Jiao, and Liangdong Xu "PointVQDM: point cloud completion via vector quantized diffusion model", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134032D (18 November 2024); https://doi.org/10.1117/12.3051321
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KEYWORDS
Point clouds

Diffusion

Education and training

Voxels

Feature fusion

Quantization

Data modeling

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