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

arXiv: https://arxiv.org/abs/2303.11963 · HTML: https://mlsanity.com/paper/2303.11963/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 53 references that are in the map.
- First posted on arXiv 21 Mar 2023.

## Builds on (most cited first, 25 of 25)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020)
- [Volume Rendering of Neural Implicit Surfaces](https://mlsanity.com/paper/2106.12052/) (2021)
- [Implicit Geometric Regularization for Learning Shapes](https://mlsanity.com/paper/2002.10099/) (2020)
- [Nerfstudio: A Modular Framework for Neural Radiance Field Development](https://mlsanity.com/paper/2302.04264/) (2023)
- [Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields](https://mlsanity.com/paper/2112.03907/) (2021)
- [NeRD: Neural Reflectance Decomposition from Image Collections](https://mlsanity.com/paper/2012.03918/) (2020)
- [SAL: Sign Agnostic Learning of Shapes from Raw Data](https://mlsanity.com/paper/1911.10414/) (2019)
- [Extracting Triangular 3D Models, Materials, and Lighting From Images](https://mlsanity.com/paper/2111.12503/) (2021)
- [PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting](https://mlsanity.com/paper/2104.00674/) (2021)
- [Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition](https://mlsanity.com/paper/2110.14373/) (2021)
- [Neural Reflectance Fields for Appearance Acquisition](https://mlsanity.com/paper/2008.03824/) (2020)
- [Modeling Indirect Illumination for Inverse Rendering](https://mlsanity.com/paper/2204.06837/) (2022)
- [Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoising](https://mlsanity.com/paper/2206.03380/) (2022)
- [TOM-Net: Learning Transparent Object Matting from a Single Image](https://mlsanity.com/paper/1803.04636/) (2018)
- [Through the Looking Glass: Neural 3D Reconstruction of Transparent Shapes](https://mlsanity.com/paper/2004.10904/) (2020)
- [Eikonal Fields for Refractive Novel-View Synthesis](https://mlsanity.com/paper/2202.00948/) (2022)
- [Full 3D Reconstruction of Transparent Objects](https://mlsanity.com/paper/1805.03482/) (2018)
- [Differentiable Refraction-Tracing for Mesh Reconstruction of Transparent Objects](https://mlsanity.com/paper/2009.09144/) (2020)
- [Hybrid Mesh-neural Representation for 3D Transparent Object Reconstruction](https://mlsanity.com/paper/2203.12613/) (2022)

## Built on by (most cited first, 14 of 14)

- [Recent Trends in 3D Reconstruction of General Non-Rigid Scenes](https://mlsanity.com/paper/2403.15064/) (2024)
- [NeRRF: 3D Reconstruction and View Synthesis for Transparent and Specular Objects with Neural Refractive-Reflective Fields](https://mlsanity.com/paper/2309.13039/) (2023)
- [TSGS: Improving Gaussian Splatting for Transparent Surface Reconstruction via Normal and De-lighting Priors](https://mlsanity.com/paper/2504.12799/) (2025)
- [Volumetric Rendering with Baked Quadrature Fields](https://mlsanity.com/paper/2312.02202/) (2023)
- [BiGS: Bidirectional Gaussian Primitives for Relightable 3D Gaussian Splatting](https://mlsanity.com/paper/2408.13370/) (2024)
- [MetaGS: A Meta-Learned Gaussian-Phong Model for Out-of-Distribution 3D Scene Relighting](https://mlsanity.com/paper/2405.20791/) (2024)
- [ESR-NeRF: Emissive Source Reconstruction Using LDR Multi-view Images](https://mlsanity.com/paper/2404.15707/) (2024)
- [PIR: Photometric Inverse Rendering with Shading Cues Modeling and Surface Reflectance Regularization](https://mlsanity.com/paper/2408.06828/) (2024)
- [From Transparent to Opaque: Rethinking Neural Implicit Surfaces with α-NeuS](https://mlsanity.com/paper/2411.05362/) (2024)
- [RT-Splatting: Joint Reflection-Transmission Modeling with Gaussian Splatting](https://mlsanity.com/paper/2605.18263/) (2026)
- [RefRef: A Dataset and Benchmark for Reconstructing Refractive and Reflective Objects](https://mlsanity.com/paper/2505.05848/) (2025)
- [OpenMaterial: A Large-scale Dataset of Complex Materials for 3D Reconstruction](https://mlsanity.com/paper/2406.08894/) (2024)
- [DiffTrans: Differentiable Geometry-Materials Decomposition for Reconstructing Transparent Objects](https://mlsanity.com/paper/2603.00413/) (2026)
- [RefracGS: Novel View Synthesis Through Refractive Water Surfaces with 3D Gaussian Ray Tracing](https://mlsanity.com/paper/2603.21695/) (2026)

## Abstract (arXiv)

We propose NEMTO, the first end-to-end neural rendering pipeline to model 3D transparent objects with complex geometry and unknown indices of refraction. Commonly used appearance modeling such as the Disney BSDF model cannot accurately address this challenging problem due to the complex light paths bending through refractions and the strong dependency of surface appearance on illumination. With 2D images of the transparent object as input, our method is capable of high-quality novel view and relighting synthesis. We leverage implicit Signed Distance Functions (SDF) to model the object geometry and propose a refraction-aware ray bending network to model the effects of light refraction within the object. Our ray bending network is more tolerant to geometric inaccuracies than traditional physically-based methods for rendering transparent objects. We provide extensive evaluations on both synthetic and real-world datasets to demonstrate our high-quality synthesis and the applicability of our method.