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3D Point Clouds Simplification Based on Low-dimensional Contour FeatureExtraction

Published: 27 June 2024 Publication History

Abstract

In order to solve the problems of low precision and poor completeness of 3D point cloud simplification, multi-view rotation projection and isometric slicing methods are used to extract low-dimensional contour features of point cloud, and the simplified model is obtained after deduplication through multiple point sets fusion. The proposed algorithm makes full use of the global and local feature information of the model, effectively retains the boundary points and feature points of the original 3D model, and improves the efficiency and accuracy of subsequent point cloud processing. This algorithm has a simple structure and can be implemented in a modular manner in practical applications. Through the experimental comparison of four models, it can be seen that this algorithm has higher accuracy and stronger robustness in terms of point cloud feature extraction and original structure retention. Experimental results show that the proposed algorithm is insensitive to the size, structure and initial position of the 3D point cloud, and the average and root mean square values of the shape deviation are better than other algorithms.

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CVIPPR '24: Proceedings of the 2024 2nd Asia Conference on Computer Vision, Image Processing and Pattern Recognition
April 2024
373 pages
ISBN:9798400716607
DOI:10.1145/3663976
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 27 June 2024

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Author Tags

  1. Coordinate projection
  2. Feature extraction
  3. Model simplification

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Overall Acceptance Rate 14 of 38 submissions, 37%

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