EigeNet: Geometry-Informed Multi-Modal Learning for Few-shot Novel View RIR Prediction
Predicting spatially varying Room Impulse Response (RIR) from sparse observations is a critical but highly challenging inverse problem for immersive spatial audio rendering. In this work, we present EIGENET, a geometry-informed multi-modal framework for few-shot novel view RIR prediction. At its core is a Cross-view Alternate-attention Transformer that iteratively refines local intra-view acoustic structures and global cross-view spatial relationships. We empirically demonstrate that this architecture is capable of making full use of the multi-view multi-modal context while performing spatial-temporal reasoning for RIR prediction. Inspired by acoustic ray tracing, we design a geometry-inf...
| Authors | Chong Jing, Zitong Lan, Junan Zhang et al. |
|---|---|
| Primary category | cs.SD |
| Published | 27 May 2026 |
| Last updated at source | 27 May 2026 |
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