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arXiv Computer Vision By Alvin Jinsung Choi, Wanhee Kim, Taeyun Kim, Dasol Hong, Wooju Lee, Hyun Myung

NeuDonatello: Uncertainty-Aware Framework for Accurate Neural SDF Learning

Read the original on arXiv Computer Vision →

NeuDonatello is a new framework for neural signed distance function (SDF) learning that explicitly models spatially varying uncertainty using Monte Carlo sampling. By incorporating this uncertainty into an adaptive regularization scheme and an uncertainty-aware SDF-to-density conversion, the method selectively strengthens geometric constraints where RGB supervision is unreliable, thereby improving surface reconstruction accuracy. Experiments show that NeuDonatello achieves state‑of‑the‑art results on diverse scenes using only posed RGB images.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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