32 citations · 78 across the 7 of their papers we have counts for
7 papers
3DGTN: 3D Dual-Attention GLocal Transformer Network for Point Cloud Classification and Segmentation
Dening Lu, Kyle Gao, Qian Xie +2
Although the application of Transformers in 3D point cloud processing has achieved significant progress and success, it is still challenging for existing 3D Transformer methods to…
MODNet: Multi-offset Point Cloud Denoising Network Customized for Multi-scale Patches
Anyi Huang, Qian Xie, Zhoutao Wang +3
The intricacy of 3D surfaces often results cutting-edge point cloud denoising (PCD) models in surface degradation including remnant noise, wrongly-removed geometric details. Althou…
Transformers in 3D Point Clouds: A Survey
Dening Lu, Qian Xie, Mingqiang Wei +3
Transformers have been at the heart of the Natural Language Processing (NLP) and Computer Vision (CV) revolutions. The significant success in NLP and CV inspired exploring the use…
Deep Algebraic Fitting for Multiple Circle Primitives Extraction from Raw Point Clouds
Zeyong Wei, Honghua Chen, Hao Tang +3
The shape of circle is one of fundamental geometric primitives of man-made engineering objects. Thus, extraction of circles from scanned point clouds is a quite important task in 3…
3DCTN: 3D Convolution-Transformer Network for Point Cloud Classification
Dening Lu, Qian Xie, Linlin Xu +1
Although accurate and fast point cloud classification is a fundamental task in 3D applications, it is difficult to achieve this purpose due to the irregularity and disorder of poin…
MLCVNet: Multi-Level Context VoteNet for 3D Object Detection
Qian Xie, Yu-Kun Lai, Jing Wu +4
In this paper, we address the 3D object detection task by capturing multi-level contextual information with the self-attention mechanism and multi-scale feature fusion. Most existi…
Surface Reconstruction with Data-driven Exemplar Priors
Oussama Remil, Qian Xie, Xingyu Xie +2
In this paper, we propose a framework to reconstruct 3D models from raw scanned points by learning the prior knowledge of a specific class of objects. Unlike previous work that heu…