Point clouds acquired through laser scanning and stereo vision techniques have been
applied in a wide range of applications, proving to be optimal sources for map** 3D urban …

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

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 …

Abstract 3D scene flow estimation aims to estimate point-wise motions between two
consecutive frames of point clouds. Superpoints, ie, points with similar geometric features …

We propose a new supervized learning framework for oversegmenting 3D point clouds into
superpoints. We cast this problem as learning deep embeddings of the local geometry and …

In this work, we focus on scene flow learning on point clouds in a self-supervised manner. A
real-world scene can be well modeled as a collection of rigidly moving parts, therefore its …

Although significant improvement has been achieved in fully autonomous driving and
semantic high-definition map (HD) domains, most of the existing 3D point cloud …

Point cloud semantic segmentation in urban scenes plays a vital role in intelligent city
modeling, autonomous driving, and urban planning. Point cloud semantic segmentation …

The multitude of airborne point clouds limits the point cloud processing efficiency.
Superpoints are grouped based on similar points, which can effectively alleviate the demand …

Scan-to-BIM of existing buildings is in high demand by the construction industry. However,
these models are costly and time-consuming to create. The automation of this process is still …