# ENVIDR: Implicit Differentiable Renderer with Neural Environment Lighting

arXiv: https://arxiv.org/abs/2303.13022 · HTML: https://mlsanity.com/paper/2303.13022/

## Key facts

- Cited by 65 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 54 references that are in the map.
- First posted on arXiv 23 Mar 2023.

## Builds on (most cited first, 30 of 36)

- [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)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [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)
- [NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction](https://mlsanity.com/paper/2106.10689/) (2021)
- [Magic3D: High-Resolution Text-to-3D Content Creation](https://mlsanity.com/paper/2211.10440/) (2022)
- [Volume Rendering of Neural Implicit Surfaces](https://mlsanity.com/paper/2106.12052/) (2021)
- [Neural Sparse Voxel Fields](https://mlsanity.com/paper/2007.11571/) (2020)
- [PlenOctrees for Real-time Rendering of Neural Radiance Fields](https://mlsanity.com/paper/2103.14024/) (2021)
- [Block-NeRF: Scalable Large Scene Neural View Synthesis](https://mlsanity.com/paper/2202.05263/) (2022)
- [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)
- [NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis](https://mlsanity.com/paper/2012.03927/) (2020)
- [MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction](https://mlsanity.com/paper/2206.00665/) (2022)
- [Baking Neural Radiance Fields for Real-Time View Synthesis](https://mlsanity.com/paper/2103.14645/) (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)
- [State of the Art on Neural Rendering](https://mlsanity.com/paper/2004.03805/) (2020)
- [DeepView: View Synthesis with Learned Gradient Descent](https://mlsanity.com/paper/1906.07316/) (2019)
- [NeX: Real-time View Synthesis with Neural Basis Expansion](https://mlsanity.com/paper/2103.05606/) (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)
- [NeRFReN: Neural Radiance Fields with Reflections](https://mlsanity.com/paper/2111.15234/) (2021)

## Built on by (most cited first, 30 of 65)

- [GIR: 3D Gaussian Inverse Rendering for Relightable Scene Factorization](https://mlsanity.com/paper/2312.05133/) (2023)
- [3D Gaussian Splatting with Deferred Reflection](https://mlsanity.com/paper/2404.18454/) (2024)
- [DiffusionRenderer: Neural Inverse and Forward Rendering with Video Diffusion Models](https://mlsanity.com/paper/2501.18590/) (2025)
- [UniRelight: Learning Joint Decomposition and Synthesis for Video Relighting](https://mlsanity.com/paper/2506.15673/) (2025)
- [Gaussian Splashing: Unified Particles for Versatile Motion Synthesis and Rendering](https://mlsanity.com/paper/2401.15318/) (2024)
- [A Diffusion Approach to Radiance Field Relighting using Multi-Illumination Synthesis](https://mlsanity.com/paper/2409.08947/) (2024)
- [EnvGS: Modeling View-Dependent Appearance with Environment Gaussian](https://mlsanity.com/paper/2412.15215/) (2024)
- [NeRF-Casting: Improved View-Dependent Appearance with Consistent Reflections](https://mlsanity.com/paper/2405.14871/) (2024)
- [UniSDF: Unifying Neural Representations for High-Fidelity 3D Reconstruction of Complex Scenes with Reflections](https://mlsanity.com/paper/2312.13285/) (2023)
- [Gaussian Splatting with NeRF-based Color and Opacity](https://mlsanity.com/paper/2312.13729/) (2023)
- [Ref-GS: Directional Factorization for 2D Gaussian Splatting](https://mlsanity.com/paper/2412.00905/) (2024)
- [SpectroMotion: Dynamic 3D Reconstruction of Specular Scenes](https://mlsanity.com/paper/2410.17249/) (2024)
- [MaterialRefGS: Reflective Gaussian Splatting with Multi-view Consistent Material Inference](https://mlsanity.com/paper/2510.11387/) (2025)
- [GeoSplatting: Towards Geometry Guided Gaussian Splatting for Physically-based Inverse Rendering](https://mlsanity.com/paper/2410.24204/) (2024)
- [MVGS: Multi-view Regulated Gaussian Splatting for Novel View Synthesis](https://mlsanity.com/paper/2410.02103/) (2024)
- [SHINOBI: Shape and Illumination using Neural Object Decomposition via BRDF Optimization In-the-wild](https://mlsanity.com/paper/2401.10171/) (2024)
- [Learning-based Multi-View Stereo: A Survey](https://mlsanity.com/paper/2408.15235/) (2024)
- [Neural Directional Encoding for Efficient and Accurate View-Dependent Appearance Modeling](https://mlsanity.com/paper/2405.14847/) (2024)
- [3iGS: Factorised Tensorial Illumination for 3D Gaussian Splatting](https://mlsanity.com/paper/2408.03753/) (2024)
- [TransparentGS: Fast Inverse Rendering of Transparent Objects with Gaussians](https://mlsanity.com/paper/2504.18768/) (2025)
- [Factored-NeuS: Reconstructing Surfaces, Illumination, and Materials of Possibly Glossy Objects](https://mlsanity.com/paper/2305.17929/) (2023)
- [Uni-Renderer: Unifying Rendering and Inverse Rendering Via Dual Stream Diffusion](https://mlsanity.com/paper/2412.15050/) (2024)
- [LuxDiT: Lighting Estimation with Video Diffusion Transformer](https://mlsanity.com/paper/2509.03680/) (2025)
- [Generative Multiview Relighting for 3D Reconstruction under Extreme Illumination Variation](https://mlsanity.com/paper/2412.15211/) (2024)
- [GUS-IR: Gaussian Splatting with Unified Shading for Inverse Rendering](https://mlsanity.com/paper/2411.07478/) (2024)
- [Spherical Voronoi: Directional Appearance as a Differentiable Partition of the Sphere](https://mlsanity.com/paper/2512.14180/) (2025)
- [TraM-NeRF: Tracing Mirror and Near-Perfect Specular Reflections through Neural Radiance Fields](https://mlsanity.com/paper/2310.10650/) (2023)
- [SViM3D: Stable Video Material Diffusion for Single Image 3D Generation](https://mlsanity.com/paper/2510.08271/) (2025)
- [NeuS-PIR: Learning Relightable Neural Surface using Pre-Integrated Rendering](https://mlsanity.com/paper/2306.07632/) (2023)
- [AniSDF: Fused-Granularity Neural Surfaces with Anisotropic Encoding for High-Fidelity 3D Reconstruction](https://mlsanity.com/paper/2410.01202/) (2024)

## Abstract (arXiv)

Recent advances in neural rendering have shown great potential for reconstructing scenes from multiview images. However, accurately representing objects with glossy surfaces remains a challenge for existing methods. In this work, we introduce ENVIDR, a rendering and modeling framework for high-quality rendering and reconstruction of surfaces with challenging specular reflections. To achieve this, we first propose a novel neural renderer with decomposed rendering components to learn the interaction between surface and environment lighting. This renderer is trained using existing physically based renderers and is decoupled from actual scene representations. We then propose an SDF-based neural surface model that leverages this learned neural renderer to represent general scenes. Our model additionally synthesizes indirect illuminations caused by inter-reflections from shiny surfaces by marching surface-reflected rays. We demonstrate that our method outperforms state-of-art methods on challenging shiny scenes, providing high-quality rendering of specular reflections while also enabling material editing and scene relighting.