# REF2-NeRF: Reflection and Refraction aware Neural Radiance Field

arXiv: https://arxiv.org/abs/2311.17116 · HTML: https://mlsanity.com/paper/2311.17116/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 64 references that are in the map.
- First posted on arXiv 28 Nov 2023.

## Builds on (most cited first, 30 of 32)

- [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)
- [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)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [pixelNeRF: Neural Radiance Fields from One or Few Images](https://mlsanity.com/paper/2012.02190/) (2020)
- [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020)
- [Nerfies: Deformable Neural Radiance Fields](https://mlsanity.com/paper/2011.12948/) (2020)
- [PlenOctrees for Real-time Rendering of Neural Radiance Fields](https://mlsanity.com/paper/2103.14024/) (2021)
- [BARF: Bundle-Adjusting Neural Radiance Fields](https://mlsanity.com/paper/2104.06405/) (2021)
- [Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields](https://mlsanity.com/paper/2112.03907/) (2021)
- [NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstruction](https://mlsanity.com/paper/2212.05231/) (2022)
- [ClearGrasp: 3D Shape Estimation of Transparent Objects for Manipulation](https://mlsanity.com/paper/1910.02550/) (2019)
- [NeRFReN: Neural Radiance Fields with Reflections](https://mlsanity.com/paper/2111.15234/) (2021)
- [Dex-NeRF: Using a Neural Radiance Field to Grasp Transparent Objects](https://mlsanity.com/paper/2110.14217/) (2021)
- [NeRF-SR: High-Quality Neural Radiance Fields using Supersampling](https://mlsanity.com/paper/2112.01759/) (2021)
- [Neural Point Catacaustics for Novel-View Synthesis of Reflections](https://mlsanity.com/paper/2301.01087/) (2023)
- [GraspNeRF: Multiview-based 6-DoF Grasp Detection for Transparent and Specular Objects Using Generalizable NeRF](https://mlsanity.com/paper/2210.06575/) (2022)
- [TransCG: A Large-Scale Real-World Dataset for Transparent Object Depth Completion and a Grasping Baseline](https://mlsanity.com/paper/2202.08471/) (2022)
- [NeurAR: Neural Uncertainty for Autonomous 3D Reconstruction with Implicit Neural Representations](https://mlsanity.com/paper/2207.10985/) (2022)
- [NeU-NBV: Next Best View Planning Using Uncertainty Estimation in Image-Based Neural Rendering](https://mlsanity.com/paper/2303.01284/) (2023)
- [Through the Looking Glass: Neural 3D Reconstruction of Transparent Shapes](https://mlsanity.com/paper/2004.10904/) (2020)
- [Active Implicit Object Reconstruction using Uncertainty-guided Next-Best-View Optimization](https://mlsanity.com/paper/2303.16739/) (2023)
- [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)
- [A4T: Hierarchical Affordance Detection for Transparent Objects Depth Reconstruction and Manipulation](https://mlsanity.com/paper/2207.04907/) (2022)
- [Looking Through the Glass: Neural Surface Reconstruction Against High Specular Reflections](https://mlsanity.com/paper/2304.08706/) (2023)
- [Efficient View Path Planning for Autonomous Implicit Reconstruction](https://mlsanity.com/paper/2209.13159/) (2022)
- [Seeing Through the Glass: Neural 3D Reconstruction of Object Inside a Transparent Container](https://mlsanity.com/paper/2303.13805/) (2023)

## Built on by (most cited first, 2 of 2)

- [RT-Splatting: Joint Reflection-Transmission Modeling with Gaussian Splatting](https://mlsanity.com/paper/2605.18263/) (2026)
- [RefracGS: Novel View Synthesis Through Refractive Water Surfaces with 3D Gaussian Ray Tracing](https://mlsanity.com/paper/2603.21695/) (2026)

## Abstract (arXiv)

Recently, significant progress has been made in the study of methods for 3D reconstruction from multiple images using implicit neural representations, exemplified by the neural radiance field (NeRF) method. Such methods, which are based on volume rendering, can model various light phenomena, and various extended methods have been proposed to accommodate different scenes and situations. However, when handling scenes with multiple glass objects, e.g., objects in a glass showcase, modeling the target scene accurately has been challenging due to the presence of multiple reflection and refraction effects. Thus, this paper proposes a NeRF-based modeling method for scenes containing a glass case. In the proposed method, refraction and reflection are modeled using elements that are dependent and independent of the viewer's perspective. This approach allows us to estimate the surfaces where refraction occurs, i.e., glass surfaces, and enables the separation and modeling of both direct and reflected light components. The proposed method requires predetermined camera poses, but accurately estimating these poses in scenes with glass objects is difficult. Therefore, we used a robotic arm with an attached camera to acquire images with known poses. Compared to existing methods, the proposed method enables more accurate modeling of both glass refraction and the overall scene.