# Explicit Context-Driven Neural Acoustic Modeling for High-Fidelity RIR Generation

arXiv: https://arxiv.org/abs/2509.15210 · HTML: https://mlsanity.com/paper/2509.15210/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 57 references that are in the map.
- First posted on arXiv 18 Sep 2025.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [pixelNeRF: Neural Radiance Fields from One or Few Images](https://mlsanity.com/paper/2012.02190/) (2020)
- [DUSt3R: Geometric 3D Vision Made Easy](https://mlsanity.com/paper/2312.14132/) (2023)
- [VGGT: Visual Geometry Grounded Transformer](https://mlsanity.com/paper/2503.11651/) (2025)
- [The Replica Dataset: A Digital Replica of Indoor Spaces](https://mlsanity.com/paper/1906.05797/) (2019)
- [KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs](https://mlsanity.com/paper/2103.13744/) (2021)
- [RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs](https://mlsanity.com/paper/2112.00724/) (2021)
- [FastNeRF: High-Fidelity Neural Rendering at 200FPS](https://mlsanity.com/paper/2103.10380/) (2021)
- [Neuralangelo: High-Fidelity Neural Surface Reconstruction](https://mlsanity.com/paper/2306.03092/) (2023)
- [Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughs](https://mlsanity.com/paper/2112.10703/) (2021)
- [3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics](https://mlsanity.com/paper/2011.09127/) (2020)
- [PGSR: Planar-based Gaussian Splatting for Efficient and High-Fidelity Surface Reconstruction](https://mlsanity.com/paper/2406.06521/) (2024)
- [Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes](https://mlsanity.com/paper/2404.10772/) (2024)
- [GANSynth: Adversarial Neural Audio Synthesis](https://mlsanity.com/paper/1902.08710/) (2019)
- [Position Information in Transformers: An Overview](https://mlsanity.com/paper/2102.11090/) (2021)
- [Learning Neural Acoustic Fields](https://mlsanity.com/paper/2204.00628/) (2022)
- [AV-NeRF: Learning Neural Fields for Real-World Audio-Visual Scene Synthesis](https://mlsanity.com/paper/2302.02088/) (2023)
- [Few-Shot Audio-Visual Learning of Environment Acoustics](https://mlsanity.com/paper/2206.04006/) (2022)
- [Image2Reverb: Cross-Modal Reverb Impulse Response Synthesis](https://mlsanity.com/paper/2103.14201/) (2021)
- [GWA: A Large High-Quality Acoustic Dataset for Audio Processing](https://mlsanity.com/paper/2204.01787/) (2022)
- [Deep Learning-based 3D Point Cloud Classification: A Systematic Survey and Outlook](https://mlsanity.com/paper/2311.02608/) (2023)
- [MESH2IR: Neural Acoustic Impulse Response Generator for Complex 3D Scenes](https://mlsanity.com/paper/2205.09248/) (2022)
- [Neural Acoustic Context Field: Rendering Realistic Room Impulse Response With Neural Fields](https://mlsanity.com/paper/2309.15977/) (2023)
- [Deep Impulse Responses: Estimating and Parameterizing Filters with Deep Networks](https://mlsanity.com/paper/2202.03416/) (2022)
- [Scene-Aware Audio Rendering via Deep Acoustic Analysis](https://mlsanity.com/paper/1911.06245/) (2019)
- [ColNeRF: Collaboration for Generalizable Sparse Input Neural Radiance Field](https://mlsanity.com/paper/2312.09095/) (2023)
- [NeRAF: 3D Scene Infused Neural Radiance and Acoustic Fields](https://mlsanity.com/paper/2405.18213/) (2024)
- [Few-shot Neural Radiance Fields Under Unconstrained Illumination](https://mlsanity.com/paper/2303.11728/) (2023)
- [3D Visibility-aware Generalizable Neural Radiance Fields for Interacting Hands](https://mlsanity.com/paper/2401.00979/) (2024)

## Abstract (arXiv)

Realistic sound simulation plays a critical role in many applications. A key element in sound simulation is the room impulse response (RIR), which characterizes how sound propagates within a given space. Recent studies have applied neural implicit methods to learn RIR using context information collected from the environment, such as scene images. However, these approaches do not effectively leverage explicit geometric information from the environment. To further exploit neural implicit models with direct geometric features, we present MiNAF, which queries a rough room mesh at given locations and extracts distance distributions as an explicit representation of local context. Our approach demonstrates that incorporating explicit local geometric features can better guide the model in generating more accurate RIR predictions. Through comparisons with conventional and state-of-the-art methods, we show that MiNAF performs competitively across various evaluation metrics.