Over the past decade deep learning has driven progress in 2D image understanding.
Despite these advancements, techniques for automatic 3D sensed data understanding, such …

To the best of our knowledge, the most recent light detection and ranging (lidar)-based
surveys have been focused only on specific applications such as reconstruction and …

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 …

Few prior works study deep learning on point sets. PointNet is a pioneer in this direction.
However, by design PointNet does not capture local structures induced by the metric space …

The Simultaneous Localization And Mapping (SLAM) problem has been well studied in the
robotics community, especially using mono, stereo cameras or depth sensors. 3D depth …

In this paper, we tackle the labeling problem for 3D point clouds. We introduce a 3D point
cloud labeling scheme based on 3D Convolutional Neural Network. Our approach …

Recent works on 3D semantic segmentation propose to exploit the synergy between images
and point clouds by processing each modality with a dedicated network and projecting …

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 introduce a novel superpoint-based transformer architecture for efficient semantic
segmentation of large-scale 3D scenes. Our method incorporates a fast algorithm to partition …

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 …