112 citations · 181 across the 14 of their papers we have counts for
14 papers
Instructor-Worker Large Language Model System for Policy Recommendation: a Case Study on Air Quality Analysis of the January 2025 Los Angeles Wildfires
Kyle Gao, Dening Lu, Liangzhi Li +4
The Los Angeles wildfires of January 2025 caused more than 250 billion dollars in damage and lasted for nearly an entire month before containment. Following our previous work, the…
Digital Twin Buildings: 3D Modeling, GIS Integration, and Visual Descriptions Using Gaussian Splatting, ChatGPT/Deepseek, and Google Maps Platform
Kyle Gao, Dening Lu, Liangzhi Li +4
Urban digital twins are virtual replicas of cities that use multi-source data and data analytics to optimize urban planning, infrastructure management, and decision-making. Towards…
Gaussian Building Mesh (GBM): Extract a Building's 3D Mesh with Google Earth and Gaussian Splatting
Kyle Gao, Liangzhi Li, Hongjie He +3
Recently released open-source pre-trained foundational image segmentation and object detection models (SAM2+GroundingDINO) allow for geometrically consistent segmentation of object…
Enhanced 3D Urban Scene Reconstruction and Point Cloud Densification using Gaussian Splatting and Google Earth Imagery
Kyle Gao, Dening Lu, Hongjie He +2
3D urban scene reconstruction and modelling is a crucial research area in remote sensing with numerous applications in academia, commerce, industry, and administration. Recent adva…
Efficient Point Transformer with Dynamic Token Aggregating for LiDAR Point Cloud Processing
Dening Lu, Jun Zhou, Kyle +3
Recently, LiDAR point cloud processing and analysis have made great progress due to the development of 3D Transformers. However, existing 3D Transformer methods usually are computa…
3D Learnable Supertoken Transformer for LiDAR Point Cloud Scene Segmentation
Dening Lu, Jun Zhou, Kyle Gao +2
3D Transformers have achieved great success in point cloud understanding and representation. However, there is still considerable scope for further development in effective and eff…
Dynamic Clustering Transformer Network for Point Cloud Segmentation
Dening Lu, Jun Zhou, Kyle Yilin Gao +4
Point cloud segmentation is one of the most important tasks in computer vision with widespread scientific, industrial, and commercial applications. The research thereof has resulte…
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…
Deep Feature-preserving Normal Estimation for Point Cloud Filtering
Dening Lu, Xuequan Lu, Yangxing Sun +1
Point cloud filtering, the main bottleneck of which is removing noise (outliers) while preserving geometric features, is a fundamental problem in 3D field. The two-step schemes inv…