JOURNAL ARTICLE

Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation

Abstract

We achieve 3D semantic scene labeling by exploring semantic relation between each point and its contextual neighbors through edges. Besides an encoder-decoder branch for predicting point labels, we construct an edge branch to hierarchically integrate point features and generate edge features. To incorporate point features in the edge branch, we establish a hierarchical graph framework, where the graph is initialized from a coarse layer and gradually enriched along the point decoding process. For each edge in the final graph, we predict a label to indicate the semantic consistency of the two connected points to enhance point prediction. At different layers, edge features are also fed into the corresponding point module to integrate contextual information for message passing enhancement in local regions. The two branches interact with each other and cooperate in segmentation. Decent experimental results on several 3D semantic labeling datasets demonstrate the effectiveness of our work. © 2019 IEEE.

Keywords:
Computer science Point cloud Segmentation Enhanced Data Rates for GSM Evolution Graph Encoder Point (geometry) Consistency (knowledge bases) Artificial intelligence Construct (python library) Theoretical computer science Pattern recognition (psychology) Mathematics

Metrics

211
Cited By
27.11
FWCI (Field Weighted Citation Impact)
44
Refs
1.00
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

3D Shape Modeling and Analysis
Physical Sciences →  Engineering →  Computational Mechanics
Computer Graphics and Visualization Techniques
Physical Sciences →  Computer Science →  Computer Graphics and Computer-Aided Design
3D Surveying and Cultural Heritage
Physical Sciences →  Earth and Planetary Sciences →  Geology

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