226 citations · 505 across the 14 of their papers we have counts for
12 papers · 1 filter
LiDAR-CS Dataset: LiDAR Point Cloud Dataset with Cross-Sensors for 3D Object Detection
Jin Fang, Dingfu Zhou, Jingjing Zhao +4
Over the past few years, there has been remarkable progress in research on 3D point clouds and their use in autonomous driving scenarios has become widespread. However, deep learni…
Multi-Sem Fusion: Multimodal Semantic Fusion for 3D Object Detection
Shaoqing Xu, Fang Li, Ziying Song +3
LiDAR and camera fusion techniques are promising for achieving 3D object detection in autonomous driving. Most multi-modal 3D object detection frameworks integrate semantic knowled…
Semi-supervised 3D Object Detection with Proficient Teachers
Junbo Yin, Jin Fang, Dingfu Zhou +4
Dominated point cloud-based 3D object detectors in autonomous driving scenarios rely heavily on the huge amount of accurately labeled samples, however, 3D annotation in the point c…
ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detection
Junbo Yin, Dingfu Zhou, Liangjun Zhang +4
Existing approaches for unsupervised point cloud pre-training are constrained to either scene-level or point/voxel-level instance discrimination. Scene-level methods tend to lose l…
AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection
Zongdai Liu, Dingfu Zhou, Feixiang Lu +2
Existing deep learning-based approaches for monocular 3D object detection in autonomous driving often model the object as a rotated 3D cuboid while the object's geometric shape has…
FusionPainting: Multimodal Fusion with Adaptive Attention for 3D Object Detection
Shaoqing Xu, Dingfu Zhou, Jin Fang +3
Accurate detection of obstacles in 3D is an essential task for autonomous driving and intelligent transportation. In this work, we propose a general multimodal fusion framework Fus…
Large Scale Autonomous Driving Scenarios Clustering with Self-supervised Feature Extraction
Jinxin Zhao, Jin Fang, Zhixian Ye +1
The clustering of autonomous driving scenario data can substantially benefit the autonomous driving validation and simulation systems by improving the simulation tests' completenes…
MapFusion: A General Framework for 3D Object Detection with HDMaps
Jin Fang, Dingfu Zhou, Xibin Song +1
3D object detection is a key perception component in autonomous driving. Most recent approaches are based on Lidar sensors only or fused with cameras. Maps (e.g., High Definition M…
IAFA: Instance-aware Feature Aggregation for 3D Object Detection from a Single Image
Dingfu Zhou, Xibin Song, Yuchao Dai +5
3D object detection from a single image is an important task in Autonomous Driving (AD), where various approaches have been proposed. However, the task is intrinsically ambiguous a…
AutoRemover: Automatic Object Removal for Autonomous Driving Videos
Rong Zhang, Wei Li, Peng Wang +7
Motivated by the need for photo-realistic simulation in autonomous driving, in this paper we present a video inpainting algorithm \emph{AutoRemover}, designed specifically for gene…
IoU Loss for 2D/3D Object Detection
Dingfu Zhou, Jin Fang, Xibin Song +4
In 2D/3D object detection task, Intersection-over-Union (IoU) has been widely employed as an evaluation metric to evaluate the performance of different detectors in the testing sta…
Augmented LiDAR Simulator for Autonomous Driving
Jin Fang, Dingfu Zhou, Feilong Yan +5
In Autonomous Driving (AD), detection and tracking of obstacles on the roads is a critical task. Deep-learning based methods using annotated LiDAR data have been the most widely ad…