LPFormer presents a novel approach to 3D human pose estimation by leveraging LiDAR data exclusively. The model consists of two stages: identifying human bounding boxes and utilizing a transformer-based network to predict keypoints. LPFormer outperforms previous methods by seamlessly integrating complex HPE tasks into a LiDAR perception network. Experimental results demonstrate superior performance on large-scale datasets, showcasing the potential of LiDAR-only solutions in 3D pose estimation.
LPFormer addresses challenges in acquiring accurate 3D annotations for HPE by relying solely on LiDAR data. The model's innovative design allows for end-to-end 3D pose estimation without the need for image features or annotations. By leveraging a transformer-based network, LPFormer achieves remarkable accuracy and outperforms existing multi-modal solutions. The model's success on the Waymo Open Dataset highlights its effectiveness in real-world scenarios.
The paper discusses the methodology behind LPFormer, detailing its two-stage architecture and the key components involved in predicting 3D keypoints from LiDAR point clouds. Through an ablation study, the authors analyze the impact of different components on overall performance, showcasing the importance of each element in enhancing accuracy.
Overall, LPFormer represents a significant advancement in 3D human pose estimation by demonstrating that accurate results can be achieved using only LiDAR input and minimal annotations.
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by Dongqiangzi ... at arxiv.org 03-05-2024
https://arxiv.org/pdf/2306.12525.pdfDeeper Inquiries