Enhancing point cloud regression for human pose estimation with hyperbolic embedding
In the domain of human pose estimation using mmWave radar-generated point cloud data, the integration of advanced neural network models and novel embedding techniques is crucial for enhancing accuracy and efficiency. This dissertation introduces a method, HyperPose, which builds upon the robust f...
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Format: | Thesis-Master by Coursework |
Language: | English |
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Nanyang Technological University
2024
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Online Access: | https://hdl.handle.net/10356/176581 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | In the domain of human pose estimation using mmWave radar-generated point
cloud data, the integration of advanced neural network models and novel embedding
techniques is crucial for enhancing accuracy and efficiency. This dissertation
introduces a method, HyperPose, which builds upon the robust feature
extraction capabilities of the PointMLP and DGCNN models, integrating hyperbolic
embedding as a novel enhancement for exceptional feature representation.
Hyperbolic space, known for its superiority in modeling hierarchical data compared
to Euclidean spaces, is utilized to address the compositional complexity
of human poses. By embedding the extracted features into hyperbolic space,
HyperPose effectively captures the complex hierarchical relationships inherent
in human poses, thereby significantly improving the accuracy of pose estimation.
Our comprehensive experiments conducted on the mm-fi dataset demonstrate
HyperPose’s competitive performance in terms of precision and computational
efficiency. Furthermore, experiment confirm the critical role of hyperbolic
embedding in enhancing the model’s ability to discern complex human poses
from mmWave radar-generated point clouds. The results not only underline the
effectiveness of HyperPose but also open new avenues for research into point
cloud-based human pose estimation and the application of hyperbolic spaces in
deep learning architectures |
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