Room Geometry Inference

Room geometry inference (RGI) aims to reconstruct the shape of a room using acoustic measurements, primarily room impulse responses (RIRs). Current research heavily utilizes deep neural networks, often convolutional recurrent networks (CRNNs), to analyze RIRs and directly infer room dimensions and shapes, even handling complex geometries and scenarios with missing or weak first-order reflections. This technology has significant implications for applications like 3D audio rendering, virtual acoustics, robotic navigation, and the creation of accurate digital twins of indoor spaces. The field is actively exploring methods to improve robustness and accuracy, particularly in challenging acoustic environments.