Gongjie Zhang
Papers
2
Total Citations
21
H-Index
2
About
Gongjie Zhang is a researcher specializing in 3D computer vision, with a particular focus on LiDAR-based perception for autonomous driving and robotics. Their work centers on advancing the understanding of 3D point cloud data, tackling challenges that are critical to the safe and reliable operation of autonomous systems. Among their most notable contributions is "TransPillars: Coarse-to-Fine Aggregation for Multi-Frame 3D Object Detection" (2023), which has garnered 15 citations. This work addresses a significant gap in the field by leveraging temporal information across point cloud sequences — moving beyond the limitations of single-frame detection methods to achieve more robust and accurate 3D object detection. Building on this trajectory, Zhang's 2024 paper "Modeling Continuous Motion for 3D Point Cloud Object Tracking" (6 citations) advances single object tracking by incorporating long-range motion modeling across multiple successive frames, rather than relying solely on pairwise frame comparisons or appearance matching. Together, these contributions reflect Zhang's overarching goal of enriching spatial perception with temporal context, pushing the boundaries of what autonomous systems can understand about dynamic environments. Their research represents a meaningful step forward in making real-world 3D perception more reliable and practically deployable.
Research Focus
Key Achievements
Top Papers
- 1TransPillars: Coarse-to-Fine Aggregation for Multi-Frame 3D Object Detection15 citations · 2023
- 2Modeling Continuous Motion for 3D Point Cloud Object Tracking6 citations · 2024
Key Collaborators
Related papers
- LiDAR-based 3D object detection and tracking for autonomous driving
- Modeling Continuous Motion for 3D Point Cloud Object Tracking
- Modeling Continuous Motion for 3D Point Cloud Object Tracking
- TransPillars: Coarse-to-Fine Aggregation for Multi-Frame 3D Object Detection
- Point cloud video object segmentation using a persistent supervoxel world-model
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