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About this paper
Jiaze Li, Daisheng Jin, Fei Hou, Junhui Hou, Zheng Liu, Shiqing Xin, Wenping Wang, Ying He
DiWR reconstructs surfaces from unoriented point clouds by jointly optimizing orientations, area weights, and confidence values in a generalized winding-number field. Area weights compensate for uneven sampling, while confidence discounts unreliable samples. In a 125-case Bunny stress test, DiWR met a visual success criterion in 88% of cases, versus baseline rates of 20% to 40%, when sufficient geometric evidence remained.
Key insights
- Explicitly optimizing per-point area weights allows the reconstruction to compensate for non-uniform sampling, while confidence coefficients enable the system to identify and down-weight unreliable noisy points and outliers.
- The Dirichlet energy regularization on the GWN field effectively promotes a smooth and harmonic implicit field, stabilizing the reconstruction process even with imperfect input data.
- Integrating compensation for sampling non-uniformity and outlier robustness directly into the reconstruction pipeline eliminates the need for separate, potentially error-prone preprocessing stages.
Summary by alphaXiv.
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