Building information modelling (BIM) is evolving significantly in the architecture, engineering
and construction industries. BIM involves various remote-sensing tools, procedures and …

As a pioneering work exploring transformer architecture for 3D point cloud understanding,
Point Transformer achieves impressive results on multiple highly competitive benchmarks. In …

Abstract 3D point cloud segmentation has made tremendous progress in recent years. Most
current methods focus on aggregating local features, but fail to directly model long-range …

The emerging field of artificial intelligence of things (AIoT, AI+ IoT) is driven by the
widespread use of intelligent infrastructures and the impressive success of deep learning …

LiDAR segmentation is crucial for autonomous driving perception. Recent trends favor point-
or voxel-based methods as they often yield better performance than the traditional range …

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 …

This article addresses the problem of distilling knowledge from a large teacher model to a
slim student network for LiDAR semantic segmentation. Directly employing previous …

State-of-the-art methods for large-scale driving-scene LiDAR segmentation often project the
point clouds to 2D space and then process them via 2D convolution. Although this …

Point cloud segmentation is fundamental in understanding 3D environments. However,
current 3D point cloud segmentation methods usually perform poorly on scene boundaries …

The recent success of neural networks enables a better interpretation of 3D point clouds, but
processing a large-scale 3D scene remains a challenging problem. Most current …