AV-DAR: Differentiable Audio-Visual Room Acoustics

• AV-DAR is a framework that synthesizes room impulse responses by integrating visual priors and differentiable acoustic rendering in a data-driven manner.
• It employs hybrid architectures including neural implicit fields, physics-based beam tracing, and transformer pipelines for precise acoustic scene synthesis.
• AV-DAR frameworks deliver high-fidelity, data-efficient, and real-time audio-visual scene editing, enabling scalable digital twins and immersive applications.

Audio-Visual Differentiable Room Acoustic Rendering (AV-DAR) constitutes a class of technical approaches designed to estimate and synthesize spatially accurate room impulse responses (RIRs) and visual scene representations in a fully differentiable, data-driven framework. By jointly exploiting multi-view vision cues and physics-informed or neural acoustic modeling, AV-DAR systems enable high-fidelity audio-visual scene synthesis, interactive editing of digital twins, and cross-modal generalization in both real and simulated environments.

1. Fundamental Formulation and Architectural Variants

AV-DAR models target the prediction of room impulse responses $h(t)$ for arbitrary source–receiver pairs $$(p_{\textrm{tx}},\,\omega_{\textrm{tx}},\,p_{\textrm{rx}},\,\omega_{\textrm{rx}})$$ using both acoustic and visual measurements. Recent frameworks adopt hybrid architectures integrating visual priors—typically from posed multi-view images, depth scans, or implicit 3D geometric fields—with differentiable acoustic rendering mechanisms.

The architectural taxonomy encompasses:

• Neural implicit fields: NeRF-based radiance and acoustic field models jointly infer scene appearance and acoustics via MLPs, grid sampling, and differentiable positional encoding (Brunetto et al., 2024, Liang et al., 2023).
• Physics-inspired differentiable renderers: Acoustic beam tracing and specular path enumeration conditioned on visual priors, with learnable per-surface reflectivity and device directivity (Jin et al., 30 Apr 2025, Lan et al., 11 Dec 2025).
• Transformer-based few-shot pipelines: Attention-based models fusing sparse visual and acoustic observations to predict RIRs for any query location, achieving data-efficient and environment-agnostic synthesis (Majumder et al., 2022, Liu et al., 14 Apr 2025).
• Occlusion-aware acoustic fields: Explicit modeling of geometry-driven acoustic attenuation using neural SDF representations, direction-aware attention, and geometry-conditioned global–local fusion (Gao et al., 2024).

2. Scene Representation, Visual Priors, and Geometry Conditioning

Central to AV-DAR is the usage of visual information to inform the acoustic field or rendering engine. Representations include:

• Volume-based grids generated by querying NeRF at voxel centers and directions, processed by deep CNNs (e.g., 3D ResNet-50) to produce global scene features ($\varphi_{\textrm{scene}}$) (Brunetto et al., 2024).
• Multi-view fusion via cross-attention transformers, which integrate per-image CNN features at sampled locations to form material-aware per-point embeddings (Jin et al., 30 Apr 2025).
• Planar segmentation with region-growing on RGB-D meshes, yielding material clusters with associated reflection curves $R_s(\nu)$, which are independently optimized (Lan et al., 11 Dec 2025).
• Geometry encoding via panoramic depth or SDF fields, extracting occlusion or blocking coefficients $\alpha(\mathbf{x})$ that modulate acoustic path transmittance (Gao et al., 2024).

These visual priors inform the acoustic renderer at multiple levels: attenuation per beam path, frequency-dependent reflection, and modulation of latent acoustic features.

3. Differentiable Acoustic Renderers: Neural, Physics-Based, and Hybrid

AV-DAR frameworks implement fully differentiable acoustic synthesis, either through explicit physics or learned neural mappings:

• Neural Acoustic Fields (NAcF): Continuous functions mapping $(X_m, d_m, X_s, d_s, t)$ to STFT-domain binaural RIRs, conditioned on high-dimensional visual scene priors (Brunetto et al., 2024).
• Specular Beam Tracing: Enumerate all valid specular paths $\{\mathcal{P}_n\}$ between source and receiver, accumulate attenuations from learned reflection coefficients, apply minimum-phase and spherical spreading, and sum across paths in time domain (Jin et al., 30 Apr 2025, Lan et al., 11 Dec 2025).
• Mask-based neural field models: Learn binaural transfer masks $(m_m, m_d)$ over source spectra per listener pose, incorporating distance, angle, and relative location to encode distance- and direction-dependent acoustic phenomena (Liang et al., 2023).
• Few-shot spectrogram fusion: Attention-weighted sum of reference RIR STFTs, modulated by geometric encoders, to generalize to unseen rooms with limited measurements (Liu et al., 14 Apr 2025).

All components, including volume rendering, positional encodings, convolutional/attention blocks, and RIR synthesis, are implemented via standard differentiable frameworks, enabling gradient flow from output losses to all submodules.

4. Loss Functions, Training Protocols, and Evaluation Criteria

AV-DAR models are trained end-to-end to minimize discrepancies between predicted and measured RIRs or spectrograms. Objective functions include:

• Waveform- and spectrogram-domain loss: Minimization of $L_2$ or $L_1$ error between predicted and ground-truth RIR waveforms or log-magnitude STFTs (Jin et al., 30 Apr 2025, Majumder et al., 2022, Brunetto et al., 2024).
• Energy Decay Curve (EDC) matching: Align Schroeder-integrated reverberation characteristics via an EDC loss term, directly optimizing for global acoustic metrics such as RT60 and C50 (Majumder et al., 2022, Liu et al., 14 Apr 2025).
• Spectral convergence and mask-based losses: Weighted combination of spectrogram reconstruction and convergence errors, with optional adversarial or envelope consistency terms (Brunetto et al., 2024, Gao et al., 2024).
• Regularization toward absorption database priors: Penalize divergence of learned reflection curves from known material properties (Lan et al., 11 Dec 2025).

Quantitative metrics for evaluation encompass:

Metric	Description	Reference
RT60 error (%)	Reverberation time relative error	(Jin et al., 30 Apr 2025Brunetto et al., 2024)
C50 error (dB)	Clarity index error in dB	(Jin et al., 30 Apr 2025Gao et al., 2024)
EDT error (sec)	Early decay time error	(Jin et al., 30 Apr 2025Liu et al., 14 Apr 2025)
Spectrogram error	$L_1$/$L_2$ distance in STFT domain	(Majumder et al., 2022Brunetto et al., 2024)
Loudness error (dB)	Difference in predicted vs. measured sound power	(Jin et al., 30 Apr 2025)

5. Data Efficiency, Generalization, and Cross-Modal Benefits

Recent AV-DAR methods demonstrate state-of-the-art performance with highly sparse measurements and strong cross-modal generalization:

• Physics-based, visual-prior conditioned renderers outperform pure neural approaches when trained with $\leq 1\%$ of available RIRs, achieving $-$16–55% relative improvements in loudness, C50, EDT, and T60 over baselines (Jin et al., 30 Apr 2025).
• Transformer-based, few-shot pipelines generalize to unseen rooms with zero retraining, delivering %%%%14$\varphi_{\textrm{scene}}$15%%%% improvement in RT60 error and $>$40% reduction in perceptual mean opinion score without dense scene sampling (Liu et al., 14 Apr 2025, Majumder et al., 2022).
• Conditioning acoustic fields on visual features yields up to 3.6% PSNR gain in novel view synthesis and up to 17% LPIPS reduction when RGB views are sparse (Brunetto et al., 2024).
• Occlusion-aware schemes uniquely recover acoustic attenuation due to walls and doors, correcting qualitative failures in earlier neural field approaches (Gao et al., 2024).

6. Limitations, Open Problems, and Future Extensions

Acknowledged limitations across AV-DAR frameworks include:

• Current beam tracing and specular path enumeration may neglect beam splitting, diffraction, and high-order diffuse reverberation, potentially under-modeling complex acoustic effects (Jin et al., 30 Apr 2025).
• Robustness depends on accurate geometry; coarse planar models or visual priors may degrade in cluttered or non-convex environments (Lan et al., 11 Dec 2025).
• Sim-to-real transfer remains challenged by noisy late reverberation and differences in measured and simulated impulse characteristics (Liu et al., 14 Apr 2025).
• Some models require known sound source positions or fail to resolve blind localization (Gao et al., 2024).

Future directions focus on:

• Integrating learned diffraction and scattering modules within differentiable renderers.
• Developing adaptive or meta-learned global priors for zero-shot generalization.
• Joint refinement of geometry and acoustic properties from audio-visual data.
• Combining wave-based solvers, adversarial losses, or hybrid analytic-neural components for realism and efficiency.

7. Practical Implementations and Real-Time Editing

Optimizations and design choices have enabled interactive AV-DAR applications such as audio-visual digital twins built entirely from commodity smartphones (Lan et al., 11 Dec 2025):

• Ray casting with only first-hit evaluation dramatically decreases computational cost ($\sim$10 ms per RIR update).
• Bandwise reflectivity parameterization and early-exit ray sampling accelerate optimization and inference.
• Visual SLAM-backed mesh generation and planar region-growing efficiently constrain the acoustic parameter space.
• Editable scene graphs support real-time modifications to geometry and material, with immediate acoustic updates via differentiable rendering.

This suggests that AV-DAR provides a foundation for scalable, interpretable, and data-efficient audio-visual scene synthesis and editing, enabling immersive mixed reality and telepresence applications grounded in physical and perceptually accurate acoustic modeling.