Background-Aware 3-D Point Cloud Segmentation With Dynamic Point Feature Aggregation

Jiajing Chen

doi:10.1109/TGRS.2022.3168555

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Background-Aware 3-D Point Cloud Segmentation With Dynamic Point Feature Aggregation

2022-01-01

Jiajing Chen *

Burak Kakillioglu

Senem Velipasalar

DOI：10.1109/TGRS.2022.3168555

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Abstract

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With the proliferation of LiDAR sensors and 3-D vision cameras, 3-D point cloud analysis has attracted significant attention in recent years. In this article, we propose a novel 3-D point cloud learning network, referred to as dynamic point feature aggregation network (DPFA-Net), by selectively performing the neighborhood feature aggregation (FA) with dynamic pooling and an attention mechanism. DPFA-Net has two variants for semantic segmentation and classification of 3-D point clouds. As the core module of the DPFA-Net, we propose an FA layer, in which features of the dynamic neighborhood of each point are aggregated via a self-attention mechanism. In contrast to other segmentation models, which aggregate features from fixed neighborhoods, our approach can aggregate features from different neighbors in different layers providing a more selective and broader view to the query points and focusing more on the relevant features in a local neighborhood. In addition, to further improve the performance of semantic segmentation, we exploit the background-foreground (BF) information and present two novel approaches, namely, two-stage BF-Net and BF regularization. Experimental results show that the proposed DPFA-Net achieves the state-of-the-art overall accuracy score of 89.22% for semantic segmentation on the Stanford large-scale 3-D Indoor Spaces (S3DIS) dataset and provides consistently satisfactory performance across different tasks of semantic segmentation, part segmentation, and 3-D object classification. Our model achieves 93.1% accuracy on the ModelNet40 dataset and provides a mean shape intersection-over-union (IoU) value of 85.5% for part segmentation on the ShapeNet-Part dataset. It is also computationally more efficient compared to other methods.

Keywords:

Three-dimensional displays

Point cloud compression

Semantics

Aggregates

Task analysis

Encoding

Convolutional neural networks

3-D

aggregation

feature

point cloud