112 citations · 181 across the 14 of their papers we have counts for
6 papers · 1 filter
NeRF: Neural Radiance Field in 3D Vision: A Comprehensive Review (Updated Post-Gaussian Splatting)
Kyle Gao, Yina Gao, Hongjie He +3
In March 2020, Neural Radiance Field (NeRF) revolutionized Computer Vision, allowing for implicit, neural network-based scene representation and novel view synthesis. NeRF models h…
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…
3DLG-Detector: 3D Object Detection via Simultaneous Local-Global Feature Learning
Baian Chen, Liangliang Nan, Haoran Xie +3
Capturing both local and global features of irregular point clouds is essential to 3D object detection (3OD). However, mainstream 3D detectors, e.g., VoteNet and its variants, eith…
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…
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…