Point cloud data have been widely explored due to its superior accuracy and robustness
under various adverse situations. Meanwhile, deep neural networks (DNNs) have achieved …

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 …

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

Densely annotating LiDAR point clouds is costly, which often restrains the scalability of fully-
supervised learning methods. In this work, we study the underexplored semi-supervised …

Recent advances in 3D semantic segmentation with deep neural networks have shown
remarkable success, with rapid performance increase on available datasets. However …

The rapid advancement of deep learning models is often attributed to their ability to leverage
massive training data. In contrast such privilege has not yet fully benefited 3D deep learning …

The intrinsic rotation invariance lies at the core of matching point clouds with handcrafted
descriptors. However, it is widely despised by recent deep matchers that obtain the rotation …

Semantic segmentation of large-scale mobile laser scanning (MLS) point clouds is essential
for urban scene understanding. However, most of the existing semantic segmentation …

Abstract We present NeRF-Det, a novel method for indoor 3D detection with posed RGB
images as input. Unlike existing indoor 3D detection methods that struggle to model scene …

Modern 3D semantic instance segmentation approaches predominantly rely on specialized
voting mechanisms followed by carefully designed geometric clustering techniques. Building …