24 citations · 53 across the 11 of their papers we have counts for
11 papers · 1 filter
End-to-End HOI Reconstruction Transformer with Graph-based Encoding
Zhenrong Wang, Qi Zheng, Sihan Ma +3
With the diversification of human-object interaction (HOI) applications and the success of capturing human meshes, HOI reconstruction has gained widespread attention. Existing main…
Hints of Prompt: Enhancing Visual Representation for Multimodal LLMs in Autonomous Driving
Hao Zhou, Zhanning Gao, Zhili Chen +4
In light of the dynamic nature of autonomous driving environments and stringent safety requirements, general MLLMs combined with CLIP alone often struggle to accurately represent d…
Learning High-resolution Vector Representation from Multi-Camera Images for 3D Object Detection
Zhili Chen, Shuangjie Xu, Maosheng Ye +4
The Bird's-Eye-View (BEV) representation is a critical factor that directly impacts the 3D object detection performance, but the traditional BEV grid representation induces quadrat…
Cross-Cluster Shifting for Efficient and Effective 3D Object Detection in Autonomous Driving
Zhili Chen, Kien T. Pham, Maosheng Ye +2
We present a new 3D point-based detector model, named Shift-SSD, for precise 3D object detection in autonomous driving. Traditional point-based 3D object detectors often employ arc…
PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous Driving
Zhili Chen, Maosheng Ye, Shuangjie Xu +2
We present a new interaction mechanism of prediction and planning for end-to-end autonomous driving, called PPAD (Iterative Interaction of Prediction and Planning Autonomous Drivin…
Bootstrap Motion Forecasting With Self-Consistent Constraints
Maosheng Ye, Jiamiao Xu, Xunnong Xu +3
We present a novel framework to bootstrap Motion forecasting with Self-consistent Constraints (MISC). The motion forecasting task aims at predicting future trajectories of vehicles…
Sparse Cross-scale Attention Network for Efficient LiDAR Panoptic Segmentation
Shuangjie Xu, Rui Wan, Maosheng Ye +2
Two major challenges of 3D LiDAR Panoptic Segmentation (PS) are that point clouds of an object are surface-aggregated and thus hard to model the long-range dependency especially fo…
DRINet++: Efficient Voxel-as-point Point Cloud Segmentation
Maosheng Ye, Rui Wan, Shuangjie Xu +2
Recently, many approaches have been proposed through single or multiple representations to improve the performance of point cloud semantic segmentation. However, these works do not…
DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation
Maosheng Ye, Shuangjie Xu, Tongyi Cao +1
We present a novel and flexible architecture for point cloud segmentation with dual-representation iterative learning. In point cloud processing, different representations have the…
TPCN: Temporal Point Cloud Networks for Motion Forecasting
Maosheng Ye, Tongyi Cao, Qifeng Chen
We propose the Temporal Point Cloud Networks (TPCN), a novel and flexible framework with joint spatial and temporal learning for trajectory prediction. Unlike existing approaches t…
HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection
Maosheng Ye, Shuangjie Xu, Tongyi Cao
We present Hybrid Voxel Network (HVNet), a novel one-stage unified network for point cloud based 3D object detection for autonomous driving. Recent studies show that 2D voxelizatio…