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

In recent years, the popularity of depth sensors and 3D scanners has led to a rapid
development of 3D point clouds. Semantic segmentation of point cloud, as a key step in …

3D semantic scene labeling is fundamental to agents operating in the real world. In
particular, labeling raw 3D point sets from sensors provides fine-grained semantics. Recent …

Abstract 3D scene analysis in terms of automatically assigning 3D points a respective
semantic label has become a topic of great importance in photogrammetry, remote sensing …

We describe an effective and efficient method for point-wise semantic classification of 3D
point clouds. The method can handle unstructured and inhomogeneous point clouds such …

In this work we address the task of the contextual classification of an airborne LiDAR point
cloud. For that purpose, we integrate a Random Forest classifier into a Conditional Random …

This papers presents a multi-scale method that computes robust geometric features on lidar
point clouds in order to retrieve the optimal neighborhood size for each point. Three …

We present a system for the detection of small and potentially obscured obstacles in
vegetated terrain. The key novelty of this system is the coupling of a volumetric occupancy …

This paper introduces a new definition of multiscale neighborhoods in 3D point clouds. This
definition, based on spherical neighborhoods and proportional subsampling, allows the …

Inexpensive RGB-D cameras that give an RGB image together with depth data have
become widely available. In this paper, we use this data to build 3D point clouds of full …