Multiscale Bilateral Aggregation Network for Point Cloud Analysis

Point cloud is unordered, irregular, and scattered and inherently lacks topological information, and it is very challenging for semantic segmentation and understanding of point cloud. Existing point cloud segmentation methods usually ignore the structural information between points, which leads to t...

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Bibliographic Details
Published in:IEEE sensors journal 2024-11, Vol.24 (21), p.34966-34976
Main Authors: Zhang, Huanhuan, Zhu, Yanqi, Wang, Lei, Pan, Quan
Format: Article
Language:English
Subjects:
Bilateral feature aggregation
Convolution
Error analysis
Feature extraction
Image segmentation
Performance evaluation
point cloud
Point cloud compression
Qualitative analysis
segmentation
Semantic segmentation
Semantics
Sensors
Shape
Three dimensional models
Three-dimensional displays
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Summary:Point cloud is unordered, irregular, and scattered and inherently lacks topological information, and it is very challenging for semantic segmentation and understanding of point cloud. Existing point cloud segmentation methods usually ignore the structural information between points, which leads to the problem of high boundary segmentation error in the results of point cloud segmentation. This article introduces a multiscale bilateral aggregation method that improves the segmentation performance of boundary regions by learning semantic and geometric features from point clouds and establishing stronger connectivity and adjacency relationships. We conduct extensive qualitative and quantitative evaluations to evaluate our proposed method's effectiveness. The experimental result shows that our method has significant advantages in terms of accuracy and robustness in segmentation tasks on several benchmark datasets, including ShapeNetPart and Stanford Large 3-D Indoor Space (S3DIS).
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2024.3452673