Real-Time Detection and Reconstruction of Smell
Alistair Pernigo*, Yunge Wen*, Dewei Feng, Wei Dai, Kaichen Zhou, Jas Brooks, Paul Pu Liang
Aiming for UIST 2026
Olfaction remains an underexplored modality in machine learning, largely due to the cost and impracticality of obtaining molecular-level data. Building on our previous work SmellNet, we present a portable pipeline for real-time odor recognition and reproduction. Our system uses a 4-channel gas sensor array to capture odors, and a transformer-based model ScentRatioNet to predict mixture ratios across a palette of 12 base odorants. A wearable olfactory interface then synthesizes and delivers the predicted scents. To support this work, we contribute a 55-class dataset of base and mixed odors, together with both computational and human evaluations. This approach moves beyond static odor libraries and represents a step toward mobile, real-world olfactory interaction.