13 citations · 28 across the 11 of their papers we have counts for
11 papers
ISAP-3D: Identity-Slot Aligned Part-Aware 3D Generation
Junlin Hao, Haoshuai Fu, Xibin Song +4
Part-aware 3D generation aims to synthesize structured objects with semantically meaningful components, yet often suffers from structural ambiguity due to identity-layout entanglem…
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…
NPC: Neural Predictive Control for Fuel-Efficient Autonomous Trucks
Jiaping Ren, Jiahao Xiang, Hongfei Gao +6
Fuel efficiency is a crucial aspect of long-distance cargo transportation by oil-powered trucks that economize on costs and decrease carbon emissions. Current predictive control me…
ESP: Extro-Spective Prediction for Long-term Behavior Reasoning in Emergency Scenarios
Dingrui Wang, Zheyuan Lai, Yuda Li +5
Emergent-scene safety is the key milestone for fully autonomous driving, and reliable on-time prediction is essential to maintain safety in emergency scenarios. However, these emer…
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…
Model2Scene: Learning 3D Scene Representation via Contrastive Language-CAD Models Pre-training
Runnan Chen, Xinge Zhu, Nenglun Chen +6
Current successful methods of 3D scene perception rely on the large-scale annotated point cloud, which is tedious and expensive to acquire. In this paper, we propose Model2Scene, a…
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…
Transformation-Equivariant 3D Object Detection for Autonomous Driving
Hai Wu, Chenglu Wen, Wei Li +3
3D object detection received increasing attention in autonomous driving recently. Objects in 3D scenes are distributed with diverse orientations. Ordinary detectors do not explicit…