A new research paper published on arXiv details a novel approach to surface reconstruction from range scans, particularly addressing challenges posed by noise and outliers. The method formulates surface reconstruction as a robust regression problem, utilizing sensor positions and visibility information that are often discarded by other neural techniques. This approach represents the surface as the zero set of a 1-Lipschitz multi-layer perceptron and employs a Huber loss for regression, demonstrating competitive performance against state-of-the-art methods on both synthetic and real-world data. AI
IMPACT This research could lead to more accurate and robust 3D modeling from sensor data, impacting fields like robotics and augmented reality.
RANK_REASON The cluster contains a single academic paper submission to arXiv. [lever_c_demoted from research: ic=1 ai=0.7]
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