Differentiable Room Acoustic Rendering with Multi-View Vision Priors (2504.21847v1)

Abstract: An immersive acoustic experience enabled by spatial audio is just as crucial as the visual aspect in creating realistic virtual environments. However, existing methods for room impulse response estimation rely either on data-demanding learning-based models or computationally expensive physics-based modeling. In this work, we introduce Audio-Visual Differentiable Room Acoustic Rendering (AV-DAR), a framework that leverages visual cues extracted from multi-view images and acoustic beam tracing for physics-based room acoustic rendering. Experiments across six real-world environments from two datasets demonstrate that our multimodal, physics-based approach is efficient, interpretable, and accurate, significantly outperforming a series of prior methods. Notably, on the Real Acoustic Field dataset, AV-DAR achieves comparable performance to models trained on 10 times more data while delivering relative gains ranging from 16.6% to 50.9% when trained at the same scale.

Differentiable Room Acoustic Rendering with Multi-View Vision Priors

The paper "Differentiable Room Acoustic Rendering with Multi-View Vision Priors" proposes a novel framework, Audio-Visual Differentiable Room Acoustic Rendering (AV-DAR), which leverages multi-view visual observations to enhance room impulse response (RIR) prediction through a physics-based differentiable methodology. This approach aims to overcome the limitations of traditional RIR estimation techniques, which are either heavily reliant on extensive data or require complex computational resources.

Overview and Core Contributions

AV-DAR integrates visual cues from multi-view images with acoustic beam tracing to create an efficient and interpretable model for accurate RIR prediction. The key contributions of this framework are:

1. Physics-Based Differentiable Rendering: The method introduces a differentiable pipeline allowing RIR learning from sparse measurements, contrasting sharply with models that require dense RIR samples. This makes it both practical and computationally efficient.
2. Beam Tracing Integration: AV-DAR employs acoustic beam tracing for RIR computation, thus optimizing the calculation of specular reflection paths. Beam tracing offers significant advantages over traditional image-source methods and ray tracing in terms of efficiency and accuracy.
3. Multi-View Vision Integration: The framework utilizes visual cues to estimate surface acoustic properties effectively. By aligning image features from different views, AV-DAR forms a robust, unified representation that informs the prediction of reflection responses.

Results and Implications

The experimental results substantiate the efficacy of AV-DAR, demonstrating substantial performance improvements over existing baselines across various metrics such as Clarity (C50), Early Decay Time (EDT), and Reverberation Time (T60). For instance, AV-DAR achieves performance levels comparable to models trained on vastly larger datasets but with significantly reduced data requirements. On the Real Acoustic Field dataset, the method delivered relative improvements ranging from 16.6% to 50.9%, underscoring its potential in real-world applications where data accessibility and computational overhead are key concerns.

Theoretical and Practical Implications

From a theoretical perspective, the integration of visual cues with differentiable acoustic rendering presents a compelling avenue for enhancing RIR prediction. This multimodal approach exploits the inherent correlations between visual appearance and acoustic properties, offering a model that is both interpretable and generalizable.

Practically, AV-DAR holds promise for immersive applications in virtual and augmented reality environments, where accurate spatial audio is paramount. The ability to efficiently predict RIRs using limited data can facilitate more natural user experiences in these digital realms.

Future Directions

Looking ahead, the methodology can be extended to multi-scene settings, enabling few-shot or zero-shot reflection prediction, which would further diminish data and computational bottlenecks. Additionally, the potential for integrating broader visual and auditory datasets could lead to models capable of implicit acoustic modeling, thus widening the applicability across diverse real-world environments.

In summary, AV-DAR offers an innovative approach to room acoustic rendering, harnessing multi-view vision inputs alongside differentiable physics-based strategies to redefine RIR prediction efficiency and accuracy. This work marks a significant step towards scalable and interpretable audio rendering methodologies applicable to complex real-world scenarios.