OrbitRIR: Frame-Averaged Few-Shot RIR Synthesis for 3D Sound Source Localization

Embodied spatial reasoning requires agents to infer 3D world states from partial sensory observations. Among these observations, room acoustics provides a particularly informative spatial signal, as Room Impulse Responses (RIRs) encode interactions between source--receiver geometry and the surrounding environment. Recent few-shot acoustic models predict RIRs from spatial geometry and sparse room context. However, existing few-shot acoustic generators lack architectural inductive biases that reflect the known receiver-yaw invariance of omnidirectional monaural RIRs. To this end, we introduce OrbitRIR, a geometry-constrained generative acoustic model incorporating this inductive bias through Frame Averaging over a receiver-yaw orbit. We evaluate OrbitRIR on few-shot RIR synthesis tasks and downstream sound source localization tasks through analysis-by-synthesis. On AcousticRooms, OrbitRIR outperforms its geometry-unconstrained counterpart. When inverted for localization, it also improves localization accuracy over learned and physics-based baselines.