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 importance 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图三维重建中取得了显著进展，通过多层感知器表示物体，提供连续的隐式曲面表示和视角依赖的辐射度。然而，当前方法通常无法准确重建反射表面，导致严重的模糊性问题。为解决这一问题，我们提出Ref-NeuS，旨在通过削弱反射表面的重要性来减少模糊性。具体而言，我们利用异常检测器在多视角上下文的引导下估计显式反射得分，以定位反射表面。随后，我们设计了一种反射感知光度损失，通过将渲染颜色建模为高斯分布，并用反射得分表示方差，自适应地降低模糊性。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，大幅超越了现有最优方法。此外，在普通表面上，我们的模型也达到了可比性能。