activity
20192024
most citedLearning for Vehicle-to-Vehicle Cooperative Perception under Lossy Communication

158 citations · 616 across the 31 of their papers we have counts for

collaborators
Showing 2022Show all

11 papers · 1 filter

cs.CV2022★ 158 cited

Learning for Vehicle-to-Vehicle Cooperative Perception under Lossy Communication

Jinlong Li, Runsheng Xu, Xinyu Liu +4

Deep learning has been widely used in the perception (e.g., 3D object detection) of intelligent vehicle driving. Due to the beneficial Vehicle-to-Vehicle (V2V) communication, the d…

cs.RO2022★ 2 cited

Automated Driving Systems Data Acquisition and Processing Platform

Xin Xia, Zonglin Meng, Xu Han +5

This paper presents an automated driving system (ADS) data acquisition and processing platform for vehicle trajectory extraction, reconstruction, and evaluation based on connected…

cs.RO2022★ 35 cited

Analyzing Infrastructure LiDAR Placement with Realistic LiDAR Simulation Library

Xinyu Cai, Wentao Jiang, Runsheng Xu +4

Recently, Vehicle-to-Everything(V2X) cooperative perception has attracted increasing attention. Infrastructure sensors play a critical role in this research field; however, how to…

cs.CV2022★ 7 cited

Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather

Jinlong Li, Runsheng Xu, Jin Ma +3

Most object detection methods for autonomous driving usually assume a consistent feature distribution between training and testing data, which is not always the case when weathers…

cs.CV2022★ 2 cited

V2XP-ASG: Generating Adversarial Scenes for Vehicle-to-Everything Perception

Hao Xiang, Runsheng Xu, Xin Xia +3

Recent advancements in Vehicle-to-Everything communication technology have enabled autonomous vehicles to share sensory information to obtain better perception performance. With th…

cs.LG2022★ 152 cited

Diffusion Models: A Comprehensive Survey of Methods and Applications

Ling Yang, Zhilong Zhang, Yang Song +6

Diffusion models have emerged as a powerful new family of deep generative models with record-breaking performance in many applications, including image synthesis, video generation,…

cs.CV2022★ 79 cited

CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers

Runsheng Xu, Zhengzhong Tu, Hao Xiang +3

Bird's eye view (BEV) semantic segmentation plays a crucial role in spatial sensing for autonomous driving. Although recent literature has made significant progress on BEV map unde…

cs.CV2022★ 1 cited

Pik-Fix: Restoring and Colorizing Old Photos

Runsheng Xu, Zhengzhong Tu, Yuanqi Du +6

Restoring and inpainting the visual memories that are present, but often impaired, in old photos remains an intriguing but unsolved research topic. Decades-old photos often suffer…

cs.RO2022

Model-Agnostic Multi-Agent Perception Framework

Runsheng Xu, Weizhe Chen, Hao Xiang +2

Existing multi-agent perception systems assume that every agent utilizes the same model with identical parameters and architecture. The performance can be degraded with different p…

cs.CV2022★ 10 cited

V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer

Runsheng Xu, Hao Xiang, Zhengzhong Tu +3

In this paper, we investigate the application of Vehicle-to-Everything (V2X) communication to improve the perception performance of autonomous vehicles. We present a robust coopera…

eess.IV2022★ 2 cited

ROMNet: Renovate the Old Memories

Runsheng Xu, Zhengzhong Tu, Yuanqi Du +5

Renovating the memories in old photos is an intriguing research topic in computer vision fields. These legacy images often suffer from severe and commingled degradations such as cr…