Summary of Pre-training a Density-aware Pose Transformer For Robust Lidar-based 3d Human Pose Estimation, by Xiaoqi An et al.
Pre-training a Density-Aware Pose Transformer for Robust LiDAR-based 3D Human Pose Estimation
by Xiaoqi An, Lin Zhao, Chen Gong, Jun Li, Jian Yang
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to 3D Human Pose Estimation (3D HPE) using LiDAR point clouds. The authors propose a density-aware pose transformer (DAPT) to extract stable keypoint representations from low-quality point clouds, and a comprehensive LiDAR human synthesis and augmentation method to pre-train the model. The proposed method is evaluated on multiple datasets, including IMU-annotated LidarHuman26M, SLOPER4D, and manually annotated Waymo Open Dataset v2.0 (Waymo), HumanM3. The results show that the proposed method outperforms existing state-of-the-art methods, achieving significant reductions in average MPJPE. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special sensors called LiDAR to figure out how people are standing or moving. It’s a big problem because the sensors can be noisy and hard to understand. The authors came up with a new way to use these sensors that doesn’t need as much extra information as other methods do. They used something called density-aware pose transformers and human synthesis to make it work better. They tested their method on lots of different datasets and found that it’s the best one so far. |
Keywords
» Artificial intelligence » Pose estimation » Transformer
