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About this paper
Hang Du, Xuejun Yan, Jingjing Wang, Di Xie, Shiliang Pu
Researchers at Hikvision Research Institute developed two "plug-and-play" modules to enhance 3D point cloud normal estimation by explicitly addressing approximation errors in surface fitting. Their approach, which includes Z-direction transformation and normal error estimation, consistently improves accuracy across synthetic and real-world datasets with minimal computational overhead, advancing current state-of-the-art methods.
Key insights
- Explicitly analyzing and minimizing the approximation error inherent in the n-jet surface fitting process is crucial for achieving more precise 3D point cloud normal estimation.
- Optimal alignment of local point cloud patches to a canonical coordinate system (e.g., Z-axis aligned with the surface normal) is vital for reducing the higher-order derivative terms and thus the approximation error during polynomial surface fitting.
- Learning a small, additive residual term for the estimated normal can effectively compensate for the remaining approximation errors, leading to a more accurate final normal vector.
Summary by alphaXiv.
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