Neural implicit surface learning has shown significant progress in multi-view 3D reconstruction, where an object is represented by multilayer perceptrons that provide continuous implicit surface representation and view-dependent radiance. However, current methods often fail to accurately reconstruct reflective surfaces, leading to severe ambiguity. To overcome this issue, we propose Ref-NeuS, which aims to reduce ambiguity by attenuating the effect of reflective surfaces. Specifically, we utilize an anomaly detector to estimate an explicit reflection score with the guidance of multi-view context to localize reflective surfaces. Afterward, we design a reflection-aware photometric loss that adaptively reduces ambiguity by modeling rendered color as a Gaussian distribution, with the reflection score representing the variance. We show that together with a reflection direction-dependent radiance, our model achieves high-quality surface reconstruction on reflective surfaces and outperforms the state-of-the-arts by a large margin. Besides, our model is also comparable on general surfaces.

翻译：神经隐式曲面学习在多视角三维重建中取得了显著进展，该类方法利用多层感知机表示物体，能够提供连续的隐式曲面表示和与视角相关的辐射度。然而，现有方法常难以准确重建反射表面，导致严重的歧义。为解决此问题，我们提出Ref-NeuS，旨在通过减弱反射表面的影响来减少歧义。具体而言，我们利用异常检测器，在多视角上下文的指导下估计显式反射分数，以定位反射表面。随后，我们设计了一种反射感知光度损失函数，通过将渲染颜色建模为高斯分布（以反射分数代表方差）来自适应地减少歧义。实验表明，结合反射方向依赖的辐射度，我们的模型在反射表面上实现了高质量曲面重建，并大幅超越现有最优方法。此外，该模型在一般表面上亦具有可比性。