activity
20182022
most citedTransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers

28 citations · 54 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CV20221 cited

Multi-View Transformer for 3D Visual Grounding

Shijia Huang, Yilun Chen, Jiaya Jia +1

The 3D visual grounding task aims to ground a natural language description to the targeted object in a 3D scene, which is usually represented in 3D point clouds. Previous works stu…

cs.CV202228 cited

TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers

Xuyang Bai, Zeyu Hu, Xinge Zhu +4

LiDAR and camera are two important sensors for 3D object detection in autonomous driving. Despite the increasing popularity of sensor fusion in this field, the robustness against i…

cs.CV202112 cited

RoadMap: A Light-Weight Semantic Map for Visual Localization towards Autonomous Driving

Tong Qin, Yuxin Zheng, Tongqing Chen +2

Accurate localization is of crucial importance for autonomous driving tasks. Nowadays, we have seen a lot of sensor-rich vehicles (e.g. Robo-taxi) driving on the street autonomousl…

cs.RO202011 cited

AVP-SLAM: Semantic Visual Mapping and Localization for Autonomous Vehicles in the Parking Lot

Tong Qin, Tongqing Chen, Yilun Chen +1

Autonomous valet parking is a specific application for autonomous vehicles. In this task, vehicles need to navigate in narrow, crowded and GPS-denied parking lots. Accurate localiz…

cs.CV2020

DSGN: Deep Stereo Geometry Network for 3D Object Detection

Yilun Chen, Shu Liu, Xiaoyong Shen +1

Most state-of-the-art 3D object detectors heavily rely on LiDAR sensors because there is a large performance gap between image-based and LiDAR-based methods. It is caused by the wa…

cs.CV2019

Fast Point R-CNN

Yilun Chen, Shu Liu, Xiaoyong Shen +1

We present a unified, efficient and effective framework for point-cloud based 3D object detection. Our two-stage approach utilizes both voxel representation and raw point cloud dat…