4 citations · 4 across the 8 of their papers we have counts for
10 papers
MAETrack: Unleashing the Potential of Pretrained Geometric Priors for 3D Single Object Tracking
Sifan Zhou, Qiwei Wang, Linyue Tan +3
Large-scale pre-training has transformed representation learning in 2D vision, yet its transferability to 3D single object tracking (SOT) remains insufficiently understood. Directl…
SAVTrack: Selective Vote Aggregation for Reliability-Aware Point Cloud Tracking
Sifan Zhou, Linyue Tan, Qiwei Wang +2
3D single object tracking (SOT) in LiDAR point clouds is essential for autonomous systems, but remains challenging under sparse and incomplete observations. In such cases, differen…
TFTrack: A Template-Free Framework for Efficient 3D Point Cloud Tracking
Zhaofeng Hu, Sifan Zhou, Jiahao Nie +3
LiDAR-based 3D Single Object Tracking (3D SOT) is critical for robotic perception and navigation and aims to localize dynamic objects across frames in sparse point clouds. Existing…
FocusTrack: One-Stage Focus-and-Suppress Framework for 3D Point Cloud Object Tracking
Sifan Zhou, Jiahao Nie, Ziyu Zhao +2
In 3D point cloud object tracking, the motion-centric methods have emerged as a promising avenue due to its superior performance in modeling inter-frame motion. However, existing t…
CompTrack: Information Bottleneck-Guided Low-Rank Dynamic Token Compression for Point Cloud Tracking
Sifan Zhou, Yichao Cao, Jiahao Nie +4
3D single object tracking (SOT) in LiDAR point clouds is a critical task in computer vision and autonomous driving. Despite great success having been achieved, the inherent sparsit…
Manboformer: Learning Gaussian Representations via Spatial-temporal Attention Mechanism
Ziyue Zhao, Qining Qi, Jianfa Ma
Compared with voxel-based grid prediction, in the field of 3D semantic occupation prediction for autonomous driving, GaussianFormer proposed using 3D Gaussian to describe scenes wi…
Sub-SA: Strengthen In-context Learning via Submodular Selective Annotation
Jian Qian, Miao Sun, Sifan Zhou +3
In-context learning (ICL) leverages in-context examples as prompts for the predictions of Large Language Models (LLMs). These prompts play a crucial role in achieving strong perfor…
Information Entropy Guided Height-aware Histogram for Quantization-friendly Pillar Feature Encoder
Sifan Zhou, Zhihang Yuan, Dawei Yang +5
Real-time and high-performance 3D object detection plays a critical role in autonomous driving and robotics. Recent pillar-based 3D object detectors have gained significant attenti…
PillarTrack:Boosting Pillar Representation for Transformer-based 3D Single Object Tracking on Point Clouds
Weisheng Xu, Sifan Zhou, Jiaqi Xiong +2
LiDAR-based 3D single object tracking (3D SOT) is a critical issue in robotics and autonomous driving. Existing 3D SOT methods typically adhere to a point-based processing pipeline…
LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object Detection
Sifan Zhou, Liang Li, Xinyu Zhang +6
Due to highly constrained computing power and memory, deploying 3D lidar-based detectors on edge devices equipped in autonomous vehicles and robots poses a crucial challenge. Being…