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:神经隐式方法已经实现了对轻微高光反射下的高质量3D物体表面的重建。然而,当我们通过玻璃拍摄目标物体时,高光反射现象经常出现在目标物体的前面。这些场景中的复杂歧义违反了多视角的一致性,使得最近的方法难以正确地重建目标物体。为了解决这个问题,我们提出了一种新的基于隐式神经渲染的表面重建框架——NeuS-HSR。在NeuS-HSR中,物体表面被参数化为一个隐式有符号距离函数(SDF)。为了减少高光反射的干扰,我们将渲染图像分解为两个外观:目标物体和辅助平面。我们设计了一个新的辅助平面模块,将物理假设和神经网络相结合,以生成辅助平面外观。大量的合成和真实数据集实验表明,NeuS-HSR在高光反射复杂条件下与最先进的方法相比,在准确性和稳健性上都有显著提高。代码可在https://github.com/JiaxiongQ/NeuS-HSR上获取。