no code implementations • 10 Mar 2024 • Zhili Chen, Kien T. Pham, Maosheng Ye, Zhiqiang Shen, Qifeng Chen
We present a new 3D point-based detector model, named Shift-SSD, for precise 3D object detection in autonomous driving.
no code implementations • 14 Nov 2023 • Zhili Chen, Maosheng Ye, Shuangjie Xu, Tongyi Cao, Qifeng Chen
Unlike existing end-to-end autonomous driving frameworks, PPAD models the interactions among ego, agents, and the dynamic environment in an autoregressive manner by interleaving the Prediction and Planning processes at every timestep, instead of a single sequential process of prediction followed by planning.
no code implementations • ICCV 2023 • Maosheng Ye, Jiamiao Xu, Xunnong Xu, Tengfei Wang, Tongyi Cao, Qifeng Chen
Also, to model the multi-modality in motion forecasting, we design a novel self-ensembling scheme to obtain accurate teacher targets to enforce the self-constraints with multi-modality supervision.
Ranked #9 on Motion Forecasting on Argoverse CVPR 2020
no code implementations • 16 Jan 2022 • Shuangjie Xu, Rui Wan, Maosheng Ye, Xiaoyi Zou, Tongyi Cao
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 for large instances, and that objects are too close to separate each other.
no code implementations • 16 Nov 2021 • Maosheng Ye, Rui Wan, Shuangjie Xu, Tongyi Cao, Qifeng Chen
The Sparse Feature Encoder extracts the local context information for each point, and the Sparse Geometry Feature Enhancement enhances the geometric properties of a sparse point cloud via multi-scale sparse projection and attentive multi-scale fusion.
no code implementations • ICCV 2021 • Maosheng Ye, Shuangjie Xu, Tongyi Cao, Qifeng Chen
By utilizing these two modules iteratively, features can be propagated between two different representations.
no code implementations • CVPR 2021 • 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.
Ranked #52 on Motion Forecasting on Argoverse CVPR 2020
no code implementations • CVPR 2020 • Maosheng Ye, Shuangjie Xu, Tongyi Cao
We present a Hybrid Voxel network that solves this problem by fusing voxel feature encoder (VFE) of different scales at point-wise level and project into multiple pseudo-image feature maps.