An essential prerequisite for unleashing the potential of supervised deep learning
algorithms in the area of 3D scene understanding is the availability of large-scale and richly …

We introduce a new outdoor urban 3D pointcloud dataset, covering a total area of 2.7 km 2,
sampled from three Swiss cities with different characteristics. The dataset is manually …

Analyzing and extracting geometric features from 3D data is a fundamental step in 3D scene
understanding. Recent works demonstrated that deep learning architectures can operate …

Given the prominence of current 3D sensors, a fine-grained analysis on the basic point
cloud data is worthy of further investigation. Particularly, real point cloud scenes can …

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 …

Semantic segmentation of 3D point cloud is a key task in numerous intelligent transportation
system applications, eg, self-driving vehicles, traffic monitoring. Due to the sparsity and …

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 …

Abstract 3D Semantic segmentation is a key element for a variety of applications in robotics
and autonomous vehicles. For such applications, 3D data are usually acquired by LiDAR …

Standard convolution is inherently limited for semantic segmentation of point cloud due to its
isotropy about features. It neglects the structure of an object, results in poor object …

Abstract 3D point cloud semantic segmentation is crucial for 3D environment perception and
scene understanding, where learning of local context in point clouds is a crucial challenge …