Abstract
Semantic segmentation of LiDAR point clouds has implications in self-driving, robots, and augmented reality, among others. In this paper, we propose a Multi-Scale Attentive Aggregation Network (MSAAN) to achieve the global consistency of point cloud feature representation and super segmentation performance. First, upon a baseline encoder-decoder architecture for point cloud segmentation, namely, RandLA-Net, an attentive skip connection was proposed to replace the commonly used concatenation to balance the encoder and decoder features of the same scales. Second, a channel attentive enhancement module was introduced to the local attention enhancement module to boost the local feature discriminability and aggregate the local channel structure information. Third, we developed a multi-scale feature aggregation method to capture the global structure of a point cloud from both the encoder and the decoder. The experimental results reported that our MSAAN significantly outperformed state-of-the-art methods, i.e., at least 15.3% mIoU improvement for scene-2 of CSPC dataset, 5.2% for scene-5 of CSPC dataset, and 6.6% for Toronto3D dataset.
Highlights
Light Detection and Ranging (LiDAR) point clouds are widely used in many 3D understanding tasks nowadays, such as classification, semantic segmentation, object detection; among them, semantic segmentation of LiDAR point clouds is a crucial step toward high-level 3D point cloud understanding, which has significant implication in automatic driving, robotics, augmented reality (AR), smart city, among others
We focus on developing effective deep learning-based models for the semantic segmentation of LiDAR points clouds, improving from recent developments outlined in the review section below
Our Multi-Scale Attentive Aggregation Network (MSAAN) significantly outperformed state-of-art methods on the CSPC and
Summary
Benefitting from the progress of modern sensor technology, high-quality point clouds can be obtained relatively . In computer vision and remote sensing, point clouds can be obtained by four main techniques including photogrammetric methods, Light Detection and Ranging (LiDAR). Systems, Red Green Blue-Depth (RGB-D) cameras, and Synthetic Aperture Radar (SAR). LiDAR point clouds are widely used in many 3D understanding tasks nowadays, such as classification, semantic segmentation, object detection; among them, semantic segmentation of LiDAR point clouds is a crucial step toward high-level 3D point cloud understanding, which has significant implication in automatic driving, robotics, augmented reality (AR), smart city, among others. We focus on developing effective deep learning-based models for the semantic segmentation of LiDAR points clouds, improving from recent developments outlined in the review section below
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