  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.
