Neural implicit methods have achieved high-quality 3D object surfaces under slight specular highlights. However, high specular reflections (HSR) often appear in front of target objects when we capture them through glasses. The complex ambiguity in these scenes violates the multi-view consistency, then makes it challenging for recent methods to reconstruct target objects correctly. To remedy this issue, we present a novel surface reconstruction framework, NeuS-HSR, based on implicit neural rendering. In NeuS-HSR, the object surface is parameterized as an implicit signed distance function (SDF). To reduce the interference of HSR, we propose decomposing the rendered image into two appearances: the target object and the auxiliary plane. We design a novel auxiliary plane module by combining physical assumptions and neural networks to generate the auxiliary plane appearance. Extensive experiments on synthetic and real-world datasets demonstrate that NeuS-HSR outperforms state-of-the-art approaches for accurate and robust target surface reconstruction against HSR. Code is available at https://github.com/JiaxiongQ/NeuS-HSR.

翻译：神经隐式方法在弱镜面高光条件下已实现高质量的三维物体表面重建。然而，当通过玻璃拍摄目标物体时，高镜面反射常出现在物体前方。此类场景中的复杂模糊性违反了多视图一致性，使得现有方法难以正确重建目标物体。为解决这一问题，我们提出了一种基于隐式神经渲染的新型表面重建框架NeuS-HSR。在NeuS-HSR中，物体表面被参数化为隐式有符号距离函数。为降低高镜面反射的干扰，我们提出将渲染图像分解为两种外观：目标物体与辅助平面。通过结合物理假设与神经网络，我们设计了一种新型辅助平面模块以生成辅助平面外观。在合成数据集与真实世界数据集上的大量实验表明，NeuS-HSR在对抗高镜面反射实现精确鲁棒的目标表面重建方面，显著优于现有最先进方法。代码已开源于https://github.com/JiaxiongQ/NeuS-HSR。