发表机构
The University of Texas at Austin; Korea Advanced Institute of Science and Technology (KAIST); NAVERLABS; ETRI（德克萨斯大学奥斯汀分校; 韩国科学技术院; NAVERLABS; 韩国电子通信研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
NeuDonatello是建模并利用SDF不确定性的新框架，通过蒙特卡洛采样、自适应正则化和不确定性感知尺度参数实现精确表面重建，在多样场景中仅用已校准RGB图像达到最优精度。
AI 中文摘要
神经表面重建已成为从多视图图像恢复高质量3D表面的强大范式。然而，仅从RGB图像恢复精确几何形状仍具挑战性，原因在于无纹理区域、遮挡和固有场景模糊性带来的不确定性。现有方法常忽略此类不确定性，导致有符号距离函数（SDF）的估计不准确。我们提出NeuDonatello，这是一个建模并利用SDF不确定性以改进表面重建的新框架。我们方法的核心是使用蒙特卡洛采样策略建模空间变化的不确定性。利用该不确定性，我们开发了自适应正则化，在RGB监督不可靠的区域选择性强化几何约束，避免错误的表面重建。我们还为SDF到密度的转换引入了不确定性感知的尺度参数，该设计以不确定性为条件，能够更精确地建模空间变化的密度。大量实验表明，NeuDonatello实现了最先进的重建精度，仅使用已校准的RGB图像，在多样场景中表现稳健。
英文摘要
Neural surface reconstruction has emerged as a powerful paradigm for recovering high-quality 3D surfaces from multi-view images. However, recovering accurate geometry solely from RGB images remains challenging due to uncertainties arising from textureless regions, occlusions, and inherent scene ambiguities. Existing methods often overlook such uncertainties, leading to inaccurate estimates of the signed distance function (SDF). We introduce NeuDonatello, a novel framework that models and leverages SDF uncertainty to improve surface reconstruction. Central to our approach is to model spatially varying uncertainty using a Monte Carlo sampling strategy. Using this uncertainty, we develop an adaptive regularization that selectively strengthens geometric constraints where RGB supervision is unreliable, avoiding incorrect surface reconstruction. We further introduce an uncertainty-aware scale parameter for the SDF-to-density conversion. Conditioned on uncertainty, this design enables more accurate modeling of spatially varying densities. Extensive experiments demonstrate that NeuDonatello achieves state-of-the-art reconstruction accuracy, with robust performance across diverse scenes using only posed RGB images.
CommentsAccepted to BMVC 2026
