12 citations · 22 across the 7 of their papers we have counts for
7 papers
F3DGS: Federated 3D Gaussian Splatting for Decentralized Multi-Agent World Modeling
Morui Zhu, Mohammad Dehghani Tezerjani, Mátyás Szántó +3
We present F3DGS, a federated 3D Gaussian Splatting framework for decentralized multi-agent 3D reconstruction. Existing 3DGS pipelines assume centralized access to all observations…
SiCP: Simultaneous Individual and Cooperative Perception for 3D Object Detection in Connected and Automated Vehicles
Deyuan Qu, Qi Chen, Tianyu Bai +5
Cooperative perception for connected and automated vehicles is traditionally achieved through the fusion of feature maps from two or more vehicles. However, the absence of feature…
Online Self-Evolving Anomaly Detection in Cloud Computing Environments
Haili Wang, Jingda Guo, Xu Ma +3
Modern cloud computing systems contain hundreds to thousands of computing and storage servers. Such a scale, combined with ever-growing system complexity, is causing a key challeng…
CoFF: Cooperative Spatial Feature Fusion for 3D Object Detection on Autonomous Vehicles
Jingda Guo, Dominic Carrillo, Sihai Tang +6
To reduce the amount of transmitted data, feature map based fusion is recently proposed as a practical solution to cooperative 3D object detection by autonomous vehicles. The preci…
DCANet: Learning Connected Attentions for Convolutional Neural Networks
Xu Ma, Jingda Guo, Sihai Tang +4
While self-attention mechanism has shown promising results for many vision tasks, it only considers the current features at a time. We show that such a manner cannot take full adva…
Low-Latency High-Level Data Sharing for Connected and Autonomous Vehicular Networks
Qi Chen, Sihai Tang, Jacob Hochstetler +5
Autonomous vehicles can combine their own data with that of other vehicles to enhance their perceptive ability, and thus improve detection accuracy and driving safety. Data sharing…
Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Clouds
Qi Chen, Sihai Tang, Qing Yang +1
Autonomous vehicles may make wrong decisions due to inaccurate detection and recognition. Therefore, an intelligent vehicle can combine its own data with that of other vehicles to…