Differentiable Room Acoustic Rendering with Multi-View Vision Priors

2025-04-30

AI Summary

Existing room impulse response (RIR) modeling in virtual environments suffers from strong data dependency, high computational cost, or poor physical interpretability. To address these limitations, this paper proposes an end-to-end trainable audio-visual acoustic rendering method. For the first time, it couples multi-view NeRF-extracted visual features with a differentiable acoustic beam-tracing physical model, enabling joint audio-visual optimization for implicit scene geometry reconstruction and RIR estimation. The approach achieves a favorable balance among physical interpretability, data efficiency, and generalization capability. On real-world datasets such as Real Acoustic Field, it reduces RIR prediction error by 16.6%–50.9% relative to prior methods. Moreover, it attains comparable performance to large-scale purely data-driven models using only one-tenth of the training data, significantly outperforming both pure learning-based and conventional geometric approaches.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Estimating room impulse response efficiently and accurately
• Combining visual cues and physics for acoustic rendering
• Reducing data dependency in room acoustic modeling

Innovation

• Leverages multi-view images for visual cues
• Uses acoustic beam tracing for physics-based rendering
• Combines multimodal data for efficient acoustic modeling