# Resounding Acoustic Fields with Reciprocity

arXiv: https://arxiv.org/abs/2510.20602 · HTML: https://mlsanity.com/paper/2510.20602/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 62 references that are in the map.
- First posted on arXiv 23 Oct 2025.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Nerfstudio: A Modular Framework for Neural Radiance Field Development](https://mlsanity.com/paper/2302.04264/) (2023)
- [PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding](https://mlsanity.com/paper/2007.10985/) (2020)
- [VirtualHome: Simulating Household Activities via Programs](https://mlsanity.com/paper/1806.07011/) (2018)
- [PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting](https://mlsanity.com/paper/2104.00674/) (2021)
- [CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding](https://mlsanity.com/paper/2203.00680/) (2022)
- [iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks](https://mlsanity.com/paper/2108.03272/) (2021)
- [Self-Supervised Learning of Audio-Visual Objects from Video](https://mlsanity.com/paper/2008.04237/) (2020)
- [SoundSpaces 2.0: A Simulation Platform for Visual-Acoustic Learning](https://mlsanity.com/paper/2206.08312/) (2022)
- [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)
- [Relighting4D: Neural Relightable Human from Videos](https://mlsanity.com/paper/2207.07104/) (2022)
- [CLAR: Contrastive Learning of Auditory Representations](https://mlsanity.com/paper/2010.09542/) (2020)
- [Learning Speaker Representations with Mutual Information](https://mlsanity.com/paper/1812.00271/) (2018)
- [GWA: A Large High-Quality Acoustic Dataset for Audio Processing](https://mlsanity.com/paper/2204.01787/) (2022)
- [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)
- [AV-RIR: Audio-Visual Room Impulse Response Estimation](https://mlsanity.com/paper/2312.00834/) (2023)
- [Real Acoustic Fields: An Audio-Visual Room Acoustics Dataset and Benchmark](https://mlsanity.com/paper/2403.18821/) (2024)
- [Sound Localization from Motion: Jointly Learning Sound Direction and Camera Rotation](https://mlsanity.com/paper/2303.11329/) (2023)
- [AV-GS: Learning Material and Geometry Aware Priors for Novel View Acoustic Synthesis](https://mlsanity.com/paper/2406.08920/) (2024)
- [Acoustic Volume Rendering for Neural Impulse Response Fields](https://mlsanity.com/paper/2411.06307/) (2024)
- [Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operators](https://mlsanity.com/paper/2308.05141/) (2023)
- [Hearing Anything Anywhere](https://mlsanity.com/paper/2406.07532/) (2024)
- [Guiding Audio Editing with Audio Language Model](https://mlsanity.com/paper/2509.21625/) (2025)

## Built on by (most cited first, 1 of 1)

- [EigeNet: Geometry-Informed Multi-Modal Learning for Few-shot Novel View RIR Prediction](https://mlsanity.com/paper/2605.28101/) (2026)

## Abstract (arXiv)

Achieving immersive auditory experiences in virtual environments requires flexible sound modeling that supports dynamic source positions. In this paper, we introduce a task called resounding, which aims to estimate room impulse responses at arbitrary emitter location from a sparse set of measured emitter positions, analogous to the relighting problem in vision. We leverage the reciprocity property and introduce Versa, a physics-inspired approach to facilitating acoustic field learning. Our method creates physically valid samples with dense virtual emitter positions by exchanging emitter and listener poses. We also identify challenges in deploying reciprocity due to emitter/listener gain patterns and propose a self-supervised learning approach to address them. Results show that Versa substantially improve the performance of acoustic field learning on both simulated and real-world datasets across different metrics. Perceptual user studies show that Versa can greatly improve the immersive spatial sound experience. Code, dataset and demo videos are available on the project website: https://waves.seas.upenn.edu/projects/versa.