SPoILeR uses multimodal pre-training to enable accurate novel view synthesis of infrared, polarimetric, and multispectral data from RGB-supervised fine-tuning on new scenes.
In: IEEE Conference on Computer Vision and Pattern Recognition Workshop
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PGU-Net is a deep unfolding network for blind cross-sensor spectral super-resolution that jointly reconstructs the HSI and learns the spectral transformation function via alternating optimization stages.
citing papers explorer
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Learning Spectral and Polarimetric Clues for One-to-Multimodal Novel View Synthesis
SPoILeR uses multimodal pre-training to enable accurate novel view synthesis of infrared, polarimetric, and multispectral data from RGB-supervised fine-tuning on new scenes.
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Physics-Guided Deep Unfolding for Blind Cross-Sensor Spectral Super-Resolution via Learning the Spectral Transformation Function
PGU-Net is a deep unfolding network for blind cross-sensor spectral super-resolution that jointly reconstructs the HSI and learns the spectral transformation function via alternating optimization stages.