185 citations · 782 across the 34 of their papers we have counts for
34 papers
VacuumVLA: Boosting VLA Capabilities via a Unified Suction and Gripping Tool for Complex Robotic Manipulation
Hui Zhou, Siyuan Huang, Minxing Li +3
Vision Language Action models have significantly advanced general purpose robotic manipulation by harnessing large scale pretrained vision and language representations. Among exist…
SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving
Xuesong Chen, Linjiang Huang, Tao Ma +3
The integration of Vision-Language Models (VLMs) into autonomous driving systems has shown promise in addressing key challenges such as learning complexity, interpretability, and c…
Exposing the Copycat Problem of Imitation-based Planner: A Novel Closed-Loop Simulator, Causal Benchmark and Joint IL-RL Baseline
Hui Zhou, Shaoshuai Shi, Hongsheng Li
Machine learning (ML)-based planners have recently gained significant attention. They offer advantages over traditional optimization-based planning algorithms. These advantages inc…
M3Net: Multimodal Multi-task Learning for 3D Detection, Segmentation, and Occupancy Prediction in Autonomous Driving
Xuesong Chen, Shaoshuai Shi, Tao Ma +4
The perception system for autonomous driving generally requires to handle multiple diverse sub-tasks. However, current algorithms typically tackle individual sub-tasks separately,…
GiT: Towards Generalist Vision Transformer through Universal Language Interface
Haiyang Wang, Hao Tang, Li Jiang +5
This paper proposes a simple, yet effective framework, called GiT, simultaneously applicable for various vision tasks only with a vanilla ViT. Motivated by the universality of the…
UniTR: A Unified and Efficient Multi-Modal Transformer for Bird's-Eye-View Representation
Haiyang Wang, Hao Tang, Shaoshuai Shi +4
Jointly processing information from multiple sensors is crucial to achieving accurate and robust perception for reliable autonomous driving systems. However, current 3D perception…
MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Querying
Shaoshuai Shi, Li Jiang, Dengxin Dai +1
Motion prediction is crucial for autonomous driving systems to understand complex driving scenarios and make informed decisions. However, this task is challenging due to the divers…
TrajectoryFormer: 3D Object Tracking Transformer with Predictive Trajectory Hypotheses
Xuesong Chen, Shaoshuai Shi, Chao Zhang +5
3D multi-object tracking (MOT) is vital for many applications including autonomous driving vehicles and service robots. With the commonly used tracking-by-detection paradigm, 3D MO…
Self-supervised Pre-training with Masked Shape Prediction for 3D Scene Understanding
Li Jiang, Zetong Yang, Shaoshuai Shi +3
Masked signal modeling has greatly advanced self-supervised pre-training for language and 2D images. However, it is still not fully explored in 3D scene understanding. Thus, this p…
Sparse Dense Fusion for 3D Object Detection
Yulu Gao, Chonghao Sima, Shaoshuai Shi +3
With the prevalence of multimodal learning, camera-LiDAR fusion has gained popularity in 3D object detection. Although multiple fusion approaches have been proposed, they can be cl…
Virtual Sparse Convolution for Multimodal 3D Object Detection
Hai Wu, Chenglu Wen, Shaoshuai Shi +2
Recently, virtual/pseudo-point-based 3D object detection that seamlessly fuses RGB images and LiDAR data by depth completion has gained great attention. However, virtual points gen…
DSVT: Dynamic Sparse Voxel Transformer with Rotated Sets
Haiyang Wang, Chen Shi, Shaoshuai Shi +5
Designing an efficient yet deployment-friendly 3D backbone to handle sparse point clouds is a fundamental problem in 3D perception. Compared with the customized sparse convolution,…
ConQueR: Query Contrast Voxel-DETR for 3D Object Detection
Benjin Zhu, Zhe Wang, Shaoshuai Shi +3
Although DETR-based 3D detectors can simplify the detection pipeline and achieve direct sparse predictions, their performance still lags behind dense detectors with post-processing…
CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds
Haiyang Wang, Lihe Ding, Shaocong Dong +5
We present a novel two-stage fully sparse convolutional 3D object detection framework, named CAGroup3D. Our proposed method first generates some high-quality 3D proposals by levera…
MTR-A: 1st Place Solution for 2022 Waymo Open Dataset Challenge -- Motion Prediction
Shaoshuai Shi, Li Jiang, Dengxin Dai +1
In this report, we present the 1st place solution for motion prediction track in 2022 Waymo Open Dataset Challenges. We propose a novel Motion Transformer framework for multimodal…
Motion Transformer with Global Intention Localization and Local Movement Refinement
Shaoshuai Shi, Li Jiang, Dengxin Dai +1
Predicting multimodal future behavior of traffic participants is essential for robotic vehicles to make safe decisions. Existing works explore to directly predict future trajectori…
3D Object Detection for Autonomous Driving: A Comprehensive Survey
Jiageng Mao, Shaoshuai Shi, Xiaogang Wang +1
Autonomous driving, in recent years, has been receiving increasing attention for its potential to relieve drivers' burdens and improve the safety of driving. In modern autonomous d…
Towards Efficient 3D Object Detection with Knowledge Distillation
Jihan Yang, Shaoshuai Shi, Runyu Ding +2
Despite substantial progress in 3D object detection, advanced 3D detectors often suffer from heavy computation overheads. To this end, we explore the potential of knowledge distill…
MPPNet: Multi-Frame Feature Intertwining with Proxy Points for 3D Temporal Object Detection
Xuesong Chen, Shaoshuai Shi, Benjin Zhu +3
Accurate and reliable 3D detection is vital for many applications including autonomous driving vehicles and service robots. In this paper, we present a flexible and high-performanc…
RBGNet: Ray-based Grouping for 3D Object Detection
Haiyang Wang, Shaoshuai Shi, Ze Yang +5
As a fundamental problem in computer vision, 3D object detection is experiencing rapid growth. To extract the point-wise features from the irregularly and sparsely distributed poin…
Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentation
Li Jiang, Shaoshuai Shi, Zhuotao Tian +4
Rapid progress in 3D semantic segmentation is inseparable from the advances of deep network models, which highly rely on large-scale annotated data for training. To address the hig…
LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector
Xiaoyang Guo, Shaoshuai Shi, Xiaogang Wang +1
Stereo-based 3D detection aims at detecting 3D object bounding boxes from stereo images using intermediate depth maps or implicit 3D geometry representations, which provides a low-…
ST3D++: Denoised Self-training for Unsupervised Domain Adaptation on 3D Object Detection
Jihan Yang, Shaoshuai Shi, Zhe Wang +2
In this paper, we present a self-training method, named ST3D++, with a holistic pseudo label denoising pipeline for unsupervised domain adaptation on 3D object detection. ST3D++ ai…
Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds
Bowen Cheng, Lu Sheng, Shaoshuai Shi +2
3D object detection in point clouds is a challenging vision task that benefits various applications for understanding the 3D visual world. Lots of recent research focuses on how to…