This paper tackles the problem of novel view audio-visual synthesis along an arbitrary trajectory in an indoor scene, given the audio-video recordings from other known trajectories of the scene. Existing methods often overlook the effect of room geometry, particularly wall occlusion to sound propagation, making them less accurate in multi-room environments. In this work, we propose a new approach called Scene Occlusion-aware Acoustic Field (SOAF) for accurate sound generation. Our approach derives a prior for sound energy field using distance-aware parametric sound-propagation modelling and then transforms it based on scene transmittance learned from the input video. We extract features from the local acoustic field centred around the receiver using a Fibonacci Sphere to generate binaural audio for novel views with a direction-aware attention mechanism. Extensive experiments on the real dataset~\emph{RWAVS} and the synthetic dataset~\emph{SoundSpaces} demonstrate that our method outperforms previous state-of-the-art techniques in audio generation. Project page: https://github.com/huiyu-gao/SOAF/.

该论文解决了在室内场景中沿任意轨迹的新视觉音频综合问题，提出了一种考虑房间几何特性和墙壁遮挡对声音传播的影响的新方法，通过从输入视频中学习场景透射率并使用距离感知的参数化声音传播建模生成准确声音，并使用基于斐波那契球的局部声学场特征提取和定向感知的注意机制生成新视角的双耳音频，通过在真实数据集和合成数据集上的广泛实验证明了该方法在音频生成方面的优越性。

SOAF: 场景遮挡感知的神经声场