28 citations · 129 across the 13 of their papers we have counts for
13 papers · 1 filter
BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object Detection
Zehui Chen, Zhenyu Li, Shiquan Zhang +3
3D object detection from multiple image views is a fundamental and challenging task for visual scene understanding. Owing to its low cost and high efficiency, multi-view 3D object…
AutoAlignV2: Deformable Feature Aggregation for Dynamic Multi-Modal 3D Object Detection
Zehui Chen, Zhenyu Li, Shiquan Zhang +3
Point clouds and RGB images are two general perceptional sources in autonomous driving. The former can provide accurate localization of objects, and the latter is denser and richer…
Towards Model Generalization for Monocular 3D Object Detection
Zhenyu Li, Zehui Chen, Ang Li +4
Monocular 3D object detection (Mono3D) has achieved tremendous improvements with emerging large-scale autonomous driving datasets and the rapid development of deep learning techniq…
Unsupervised Domain Adaptation for Monocular 3D Object Detection via Self-Training
Zhenyu Li, Zehui Chen, Ang Li +4
Monocular 3D object detection (Mono3D) has achieved unprecedented success with the advent of deep learning techniques and emerging large-scale autonomous driving datasets. However,…
Graph-DETR3D: Rethinking Overlapping Regions for Multi-View 3D Object Detection
Zehui Chen, Zhenyu Li, Shiquan Zhang +3
3D object detection from multiple image views is a fundamental and challenging task for visual scene understanding. Due to its low cost and high efficiency, multi-view 3D object de…
SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representations
Zhenyu Li, Zehui Chen, Ang Li +6
Pre-training has become a standard paradigm in many computer vision tasks. However, most of the methods are generally designed on the RGB image domain. Due to the discrepancy betwe…
AutoAlign: Pixel-Instance Feature Aggregation for Multi-Modal 3D Object Detection
Zehui Chen, Zhenyu Li, Shiquan Zhang +5
Object detection through either RGB images or the LiDAR point clouds has been extensively explored in autonomous driving. However, it remains challenging to make these two data sou…
Monocular 3D Object Detection: An Extrinsic Parameter Free Approach
Yunsong Zhou, Yuan He, Hongzi Zhu +3
Monocular 3D object detection is an important task in autonomous driving. It can be easily intractable where there exists ego-car pose change w.r.t. ground plane. This is common du…
Multimodal Motion Prediction with Stacked Transformers
Yicheng Liu, Jinghuai Zhang, Liangji Fang +2
Predicting multiple plausible future trajectories of the nearby vehicles is crucial for the safety of autonomous driving. Recent motion prediction approaches attempt to achieve suc…
Dynamic and Static Context-aware LSTM for Multi-agent Motion Prediction
Chaofan Tao, Qinhong Jiang, Lixin Duan +1
Multi-agent motion prediction is challenging because it aims to foresee the future trajectories of multiple agents (\textit{e.g.} pedestrians) simultaneously in a complicated scene…
Recursive Social Behavior Graph for Trajectory Prediction
Jianhua Sun, Qinhong Jiang, Cewu Lu
Social interaction is an important topic in human trajectory prediction to generate plausible paths. In this paper, we present a novel insight of group-based social interaction mod…
Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation
Jiaming Sun, Linghao Chen, Yiming Xie +4
In this paper, we propose a novel system named Disp R-CNN for 3D object detection from stereo images. Many recent works solve this problem by first recovering a point cloud with di…
TPNet: Trajectory Proposal Network for Motion Prediction
Liangji Fang, Qinhong Jiang, Jianping Shi +1
Making accurate motion prediction of the surrounding traffic agents such as pedestrians, vehicles, and cyclists is crucial for autonomous driving. Recent data-driven motion predict…