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 …

Automation in point cloud data processing is central in knowledge discovery within decision-
making systems. The definition of relevant features is often key for segmentation and …

Abstract Terrestrial Laser Scanning (TLS) has been increasingly used in forestry
applications including forest inventory and plant ecology. Tree biophysical properties such …

We propose a surface fitting method for unstructured 3D point clouds. This method, called
DeepFit, incorporates a neural network to learn point-wise weights for weighted least …

Deep generative architectures provide a way to model not only images but also complex, 3-
dimensional objects, such as point clouds. In this work, we present a novel method to obtain …

In this paper, we propose a normal estimation method for unstructured 3D point clouds. This
method, called Nesti-Net, builds on a new local point cloud representation which consists of …

LiDAR occupies a vital position in self-driving as the advanced detection technology
enables autonomous vehicles (AVs) to obtain much environmental information. Ground …

LiDAR panoptic segmentation is a newly proposed technical task for autonomous driving. In
contrast to popular end-to-end deep learning solutions, we propose a hybrid method with an …

Background Individual tree extraction from terrestrial laser scanning (TLS) data is a
prerequisite for tree-scale estimations of forest biophysical properties. This task currently is …