提出可学习3D几何不确定性场，无需真实网格即可评估神经表面重建质量。
GURecon: Learning Detailed 3D Geometric Uncertainties for Neural Surface Reconstruction
- 基于几何一致性建立连续3D不确定性场，通过在线蒸馏训练。
- 在多个数据集上显著提升几何不确定性建模效果，支持增量重建等下游任务。
- 不依赖光照信息，适合集成到各类神经表面重建方法中使用。
神经表面表示在新视角合成和3D重建领域取得了显著进展，但缺乏真实网格时评估几何质量仍是一大挑战，原因在于其基于渲染的优化过程以及外观与几何通过光度损失耦合学习。本文提出GURecon框架，基于几何一致性为神经表面建立几何不确定性场。不同于依赖渲染测量的现有方法，GURecon建模连续3D不确定性场，并通过无真实几何监督的在线蒸馏方法学习。此外，为减少光照对几何一致性的影响，引入解耦场以微调不确定性场。在多个数据集上的实验表明，GURecon在建模3D几何不确定性方面具有优势，并可作为即插即用模块扩展至多种神经表面表示，提升增量重建等下游任务性能。代码与补充材料见项目主页：https://zju3dv.github.io/GURecon/。
原文摘要 · Abstract (English)
Neural surface representation has demonstrated remarkable success in the areas of novel view synthesis and 3D reconstruction. However, assessing the geometric quality of 3D reconstructions in the absence of ground truth mesh remains a significant challenge, due to its rendering-based optimization process and entangled learning of appearance and geometry with photometric losses. In this paper, we present a novel framework, i.e, GURecon, which establishes a geometric uncertainty field for the neural surface based on geometric consistency. Different from existing methods that rely on rendering-based measurement, GURecon models a continuous 3D uncertainty field for the reconstructed surface, and is learned by an online distillation approach without introducing real geometric information for supervision. Moreover, in order to mitigate the interference of illumination on geometric consistency, a decoupled field is learned and exploited to finetune the uncertainty field. Experiments on various datasets demonstrate the superiority of GURecon in modeling 3D geometric uncertainty, as well as its plug-and-play extension to various neural surface representations and improvement on downstream tasks such as incremental reconstruction. The code and supplementary material are available on the project website: https://zju3dv.github.io/GURecon/.
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