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We propose NEMTO, the first end-to-end neural rendering pipeline to model 3Dtransparent objects with complex geometry and unknown indices of refraction.Commonly used appearance modeling such as the Disney BSDF model cannotaccurately address this challenging problem due to the complex light pathsbending through refractions and the strong dependency of surface appearance onillumination. With 2D images of the transparent object as input, our method iscapable of high-quality novel view and relighting synthesis. We leverageimplicit Signed Distance Functions (SDF) to model the object geometry andpropose a refraction-aware ray bending network to model the effects of lightrefraction within the object. Our ray bending network is more tolerant togeometric inaccuracies than traditional physically-based methods for renderingtransparent objects. We provide extensive evaluations on both synthetic andreal-world datasets to demonstrate our high-quality synthesis and theapplicability of our method.

NEMTO: Neural Environment Matting for Novel View and Relighting Synthesis of Transparent Objects