Dense 2D-3D Indoor Prediction with Sound via Aligned Cross-Modal
  Distillation
Heeseung Yun Joonil Na Gunhee Kim
Sound can convey significant information for spatial reasoning in our daily
lives. To endow deep networks with such ability, we address the challenge of
dense indoor prediction with sound in both 2D and 3D via cross-modal knowledge
distillation. In this work, we propose a Spatial Alignment via Matching (SAM)
distillation framework that elicits local correspondence between the two
modalities in vision-to-audio knowledge transfer. SAM integrates audio features
with visually coherent learnable spatial embeddings to resolve inconsistencies
in multiple layers of a student model. Our approach does not rely on a specific
input representation, allowing for flexibility in the input shapes or
dimensions without performance degradation. With a newly curated benchmark
named Dense Auditory Prediction of Surroundings (DAPS), we are the first to
tackle dense indoor prediction of omnidirectional surroundings in both 2D and
3D with audio observations. Specifically, for audio-based depth estimation,
semantic segmentation, and challenging 3D scene reconstruction, the proposed
distillation framework consistently achieves state-of-the-art performance
across various metrics and backbone architectures.