# Planar Reflection-Aware Neural Radiance Fields

arXiv: https://arxiv.org/abs/2411.04984 · HTML: https://mlsanity.com/paper/2411.04984/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 30 references that are in the map.
- First posted on arXiv 7 Nov 2024.

## Builds on (most cited first, 17 of 17)

- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020)
- [Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction](https://mlsanity.com/paper/2111.11215/) (2021)
- [Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations](https://mlsanity.com/paper/1906.01618/) (2019)
- [Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes](https://mlsanity.com/paper/2011.13084/) (2020)
- [Dynamic View Synthesis from Dynamic Monocular Video](https://mlsanity.com/paper/2105.06468/) (2021)
- [DeepView: View Synthesis with Learned Gradient Descent](https://mlsanity.com/paper/1906.07316/) (2019)
- [Free View Synthesis](https://mlsanity.com/paper/2008.05511/) (2020)
- [DeepStereo: Learning to Predict New Views from the World's Imagery](https://mlsanity.com/paper/1506.06825/) (2015)
- [NeRO: Neural Geometry and BRDF Reconstruction of Reflective Objects from Multiview Images](https://mlsanity.com/paper/2305.17398/) (2023)
- [NeRFReN: Neural Radiance Fields with Reflections](https://mlsanity.com/paper/2111.15234/) (2021)
- [Extreme View Synthesis](https://mlsanity.com/paper/1812.04777/) (2018)
- [SpecNeRF: Gaussian Directional Encoding for Specular Reflections](https://mlsanity.com/paper/2312.13102/) (2023)
- [NeurMiPs: Neural Mixture of Planar Experts for View Synthesis](https://mlsanity.com/paper/2204.13696/) (2022)
- [Mirror-NeRF: Learning Neural Radiance Fields for Mirrors with Whitted-Style Ray Tracing](https://mlsanity.com/paper/2308.03280/) (2023)

## Built on by (most cited first, 6 of 6)

- [Reconstructing 4D Spatial Intelligence: A Survey](https://mlsanity.com/paper/2507.21045/) (2025)
- [GS-2DGS: Geometrically Supervised 2DGS for Reflective Object Reconstruction](https://mlsanity.com/paper/2506.13110/) (2025)
- [RT-Splatting: Joint Reflection-Transmission Modeling with Gaussian Splatting](https://mlsanity.com/paper/2605.18263/) (2026)
- [TR-Gaussians: High-fidelity Real-time Rendering of Planar Transmission and Reflection with 3D Gaussian Splatting](https://mlsanity.com/paper/2511.13009/) (2025)
- [TransmissiveGS: Residual-Guided Disentangled Gaussian Splatting for Transmissive Scene Reconstruction and Rendering](https://mlsanity.com/paper/2605.10705/) (2026)
- [Eulerian Motion Reconstruction for Water Scenery](https://mlsanity.com/paper/2609.38622/) (2026)

## Abstract (arXiv)

Neural Radiance Fields (NeRF) have demonstrated exceptional capabilities in reconstructing complex scenes with high fidelity. However, NeRF's view dependency can only handle low-frequency reflections. It falls short when handling complex planar reflections, often interpreting them as erroneous scene geometries and leading to duplicated and inaccurate scene representations. To address this challenge, we introduce a reflection-aware NeRF that jointly models planar reflectors, such as windows, and explicitly casts reflected rays to capture the source of the high-frequency reflections. We query a single radiance field to render the primary color and the source of the reflection. We propose a sparse edge regularization to help utilize the true sources of reflections for rendering planar reflections rather than creating a duplicate along the primary ray at the same depth. As a result, we obtain accurate scene geometry. Rendering along the primary ray results in a clean, reflection-free view, while explicitly rendering along the reflected ray allows us to reconstruct highly detailed reflections. Our extensive quantitative and qualitative evaluations of real-world datasets demonstrate our method's enhanced performance in accurately handling reflections.