Ripe with possibilities offered by deep-learning techniques and useful in applications
related to remote sensing, computer vision, and robotics, 3D point cloud semantic …

Mobile Laser Scanning (MLS) is a versatile remote sensing technology based on Light
Detection and Ranging (lidar) technology that has been utilized for a wide range of …

Point cloud segmentation is fundamental in understanding 3D environments. However,
current 3D point cloud segmentation methods usually perform poorly on scene boundaries …

We study the problem of efficient semantic segmentation for large-scale 3D point clouds. By
relying on expensive sampling techniques or computationally heavy pre/post-processing …

How to learn effective features from large-scale point clouds for semantic segmentation has
attracted increasing attention in recent years. Addressing this problem, we propose a …

We propose a novel deep learning-based framework to tackle the challenge of semantic
segmentation of large-scale point clouds of millions of points. We argue that the organization …

We study the problem of efficient semantic segmentation of large-scale 3D point clouds. By
relying on expensive sampling techniques or computationally heavy pre/post-processing …

This paper introduces a new urban point cloud dataset for automatic segmentation and
classification acquired by mobile laser scanning (MLS). We describe how the dataset is …

Given the prominence of 3D sensors in recent years, 3D point cloud scene data are worthy
to be further investigated. Point cloud scene understanding is a challenging task because of …

Nowadays, point clouds acquired through laser scanning and stereo matching have
deemed to be one of the best sources for mapping urban scenes. Spatial coordinates of 3-D …