IPCONV: Convolution with Multiple Different Kernels for Point Cloud Semantic Segmentation

The segmentation of airborne laser scanning (ALS) point clouds remains a challenge in remote sensing and photogrammetry. Deep learning methods, such as KPCONV, have proven effective on various datasets. However, the rigid convolutional kernel strategy of KPCONV limits its potential use for 3D object...

Full description

Bibliographic Details
Main Authors: Ruixiang Zhang, Siyang Chen, Xuying Wang, Yunsheng Zhang
Format: Article
Language:English
Published: MDPI AG 2023-10-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/15/21/5136
Description
Summary:The segmentation of airborne laser scanning (ALS) point clouds remains a challenge in remote sensing and photogrammetry. Deep learning methods, such as KPCONV, have proven effective on various datasets. However, the rigid convolutional kernel strategy of KPCONV limits its potential use for 3D object segmentation due to its uniform approach. To address this issue, we propose an Integrated Point Convolution (IPCONV) based on KPCONV, which utilizes two different convolution kernel point generation strategies, one cylindrical and one a spherical cone, for more efficient learning of point cloud data features. We propose a customizable Multi-Shape Neighborhood System (MSNS) to balance the relationship between these convolution kernel point generations. Experiments on the ISPRS benchmark dataset, LASDU dataset, and DFC2019 dataset demonstrate the validity of our method.
ISSN:2072-4292