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 …

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

Recent advances in modeling 3D objects mostly rely on synthetic datasets due to the lack of
large-scale real-scanned 3D databases. To facilitate the development of 3D perception …

Being data-driven is one of the most iconic properties of deep learning algorithms. The birth
of ImageNet drives a remarkable trend of" learning from large-scale data" in computer vision …

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 computer vision, graphics, and machine learning research groups have given a
significant amount of focus to 3D object recognition (segmentation, detection, and …

Pre-training by numerous image data has become de-facto for robust 2D representations. In
contrast, due to the expensive data processing, a paucity of 3D datasets severely hinders …

Open-vocabulary scene understanding aims to localize and recognize unseen categories
beyond the annotated label space. The recent breakthrough of 2D open-vocabulary …

Manual annotation of large-scale point cloud dataset for varying tasks such as 3D object
classification, segmentation and detection is often laborious owing to the irregular structure …

Point cloud analysis is challenging due to irregularity and unordered data structure. To
capture the 3D geometries, prior works mainly rely on exploring sophisticated local …