92 citations · 160 across the 18 of their papers we have counts for
18 papers
Rethinking the Encoding and Annotating of 3D Bounding Box: Corner-Aware 3D Object Detection from Point Clouds
Qinghao Meng, Junbo Yin, Jianbing Shen +1
Center-aligned regression remains dominant in LiDAR-based 3D object detection, yet it suffers from fundamental instability: object centers often fall in sparse or empty regions of…
OLiDM: Object-aware LiDAR Diffusion Models for Autonomous Driving
Tianyi Yan, Junbo Yin, Xianpeng Lang +3
To enhance autonomous driving safety in complex scenarios, various methods have been proposed to simulate LiDAR point cloud data. Nevertheless, these methods often face challenges…
ALOcc: Adaptive Lifting-Based 3D Semantic Occupancy and Cost Volume-Based Flow Predictions
Dubing Chen, Jin Fang, Wencheng Han +5
3D semantic occupancy and flow prediction are fundamental to spatiotemporal scene understanding. This paper proposes a vision-based framework with three targeted improvements. Firs…
RepVF: A Unified Vector Fields Representation for Multi-task 3D Perception
Chunliang Li, Wencheng Han, Junbo Yin +2
Concurrent processing of multiple autonomous driving 3D perception tasks within the same spatiotemporal scene poses a significant challenge, in particular due to the computational…
IS-Fusion: Instance-Scene Collaborative Fusion for Multimodal 3D Object Detection
Junbo Yin, Jianbing Shen, Runnan Chen +4
Bird's eye view (BEV) representation has emerged as a dominant solution for describing 3D space in autonomous driving scenarios. However, objects in the BEV representation typicall…
DI-V2X: Learning Domain-Invariant Representation for Vehicle-Infrastructure Collaborative 3D Object Detection
Li Xiang, Junbo Yin, Wei Li +3
Vehicle-to-Everything (V2X) collaborative perception has recently gained significant attention due to its capability to enhance scene understanding by integrating information from…
Self-Supervised Monocular Depth Estimation by Direction-aware Cumulative Convolution Network
Wencheng Han, Junbo Yin, Jianbing Shen
Monocular depth estimation is known as an ill-posed task in which objects in a 2D image usually do not contain sufficient information to predict their depth. Thus, it acts differen…
Language-Guided 3D Object Detection in Point Cloud for Autonomous Driving
Wenhao Cheng, Junbo Yin, Wei Li +2
This paper addresses the problem of 3D referring expression comprehension (REC) in autonomous driving scenario, which aims to ground a natural language to the targeted region in Li…
LWSIS: LiDAR-guided Weakly Supervised Instance Segmentation for Autonomous Driving
Xiang Li, Junbo Yin, Botian Shi +3
Image instance segmentation is a fundamental research topic in autonomous driving, which is crucial for scene understanding and road safety. Advanced learning-based approaches ofte…
SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud
Yan Wang, Junbo Yin, Wei Li +3
LiDAR-based 3D object detection is an indispensable task in advanced autonomous driving systems. Though impressive detection results have been achieved by superior 3D detectors, th…
Graph Neural Network and Spatiotemporal Transformer Attention for 3D Video Object Detection from Point Clouds
Junbo Yin, Jianbing Shen, Xin Gao +2
Previous works for LiDAR-based 3D object detection mainly focus on the single-frame paradigm. In this paper, we propose to detect 3D objects by exploiting temporal information in m…
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…
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…
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…
LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention
Junbo Yin, Jianbing Shen, Chenye Guan +2
Existing LiDAR-based 3D object detectors usually focus on the single-frame detection, while ignoring the spatiotemporal information in consecutive point cloud frames. In this paper…
A Unified Object Motion and Affinity Model for Online Multi-Object Tracking
Junbo Yin, Wenguan Wang, Qinghao Meng +2
Current popular online multi-object tracking (MOT) solutions apply single object trackers (SOTs) to capture object motions, while often requiring an extra affinity network to assoc…
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…