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 …

Semantic segmentation of LiDAR point clouds is an important task in autonomous driving.
However, training deep models via conventional supervised methods requires large …

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 …

Point cloud semantic segmentation often requires largescale annotated training data, but
clearly, point-wise labels are too tedious to prepare. While some recent methods propose to …

Point cloud analysis has received much attention recently; and segmentation is one of the
most important tasks. The success of existing approaches is attributed to deep network …

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 …

Point cloud semantic segmentation is a crucial task in 3D scene understanding. Existing
methods mainly focus on employing a large number of annotated labels for supervised …

Leaf‐wood separation in terrestrial LiDAR data is a prerequisite for non‐destructively
estimating biophysical forest properties such as standing wood volumes and leaf area …