This paper is not motivated to seek innovation within the attention mechanism. Instead it
focuses on overcoming the existing trade-offs between accuracy and efficiency within the …

We introduce Open3DIS a novel solution designed to tackle the problem of Open-
Vocabulary Instance Segmentation within 3D scenes. Objects within 3D environments …

Abstract Purpose of Review This paper provides an overview of integrating artificial
intelligence (AI), particularly deep learning (DL), with ground-based LiDAR point clouds for …

In 3D Referring Expression Segmentation (3D-RES), the earlier approach adopts a two-
stage paradigm, extracting segmentation proposals and then matching them with referring …

We introduce a highly efficient method for panoptic segmentation of large 3D point clouds by
redefining this task as a scalable graph clustering problem. This approach can be trained …

With the advancement of 3D sensing technologies, point clouds are gradually becoming the
main type of data representation in applications such as autonomous driving, robotics, and …

This paper addresses the challenge of 3D instance segmentation by simultaneously
leveraging 3D geometric and multi-view image information. Many previous works have …

The escalating costs of labeling 3-D point clouds have prompted researchers to investigate
weakly supervised semantic segmentation. Current methods predominantly focus on …

Given the prominence of 3-D sensors in recent years, 3-D point clouds are worthy to be
further investigated for environment perception and scene understanding. Learning accurate …

Constructing geometric digital twins of highways at present still demands substantial human
effort. Unlike most previous work that uses deep learning models to segment point clouds of …