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Immersive Spatial Audio Reproduction for VR/AR Using Room Acoustic Modelling from 360° Images

机译:使用360°影像的室内声学建模进行VR / AR的沉浸式空间音频复制

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Recent progresses in Virtual Reality (VR) and Augmented Reality (AR) allow us to experience various VR/AR applications in our daily life. In order to maximise the immersiveness of user in VR/AR environments, a plausible spatial audio reproduction synchronised with visual information is essential. In this paper, we propose a simple and efficient system to estimate room acoustic for plausible reproducton of spatial audio using 360° cameras for VR/AR applications. A pair of 360° images is used for room geometry and acoustic property estimation. A simplified 3D geometric model of the scene is estimated by depth estimation from captured images and semantic labelling using a convolutional neural network (CNN). The real environment acoustics are characterised by frequency-dependent acoustic predictions of the scene. Spatially synchronised audio is reproduced based on the estimated geometric and acoustic properties in the scene. The reconstructed scenes are rendered with synthesised spatial audio as VR/AR content. The results of estimated room geometry and simulated spatial audio are evaluated against the actual measurements and audio calculated from ground-truth Room Impulse Responses (RIRs) recorded in the rooms.
机译:虚拟现实(VR)和增强现实(AR)的最新进展使我们能够在日常生活中体验各种VR / AR应用程序。为了使用户在VR / AR环境中的沉浸感最大化,与视觉信息同步的合理的空间音频再现至关重要。在本文中,我们提出了一种简单有效的系统,可使用用于VR / AR应用的360°摄像机估算室内声学效果,以合理再现空间音频。一对360°图像用于房间几何形状和声学特性估计。通过使用卷积神经网络(CNN)从捕获的图像进行深度估计并进行语义标记,可以估算出场景的简化3D几何模型。真实的环境声学的特征在于场景的频率相关的声学预测。根据场景中估计的几何和声学特性,再现空间同步的音频。用合成的空间音频作为VR / AR内容渲染重建的场景。根据实际测量结果评估房间几何形状和模拟空间音频的结果,并根据记录在房间中的地面真实房间冲激响应(RIR)计算出音频。

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