Learning Spectral and Polarimetric Clues for One-to-Multimodal Novel View Synthesis
This work addresses the practical challenge of generating multimodal renderings from limited data, reducing the need for costly specialized sensors.
SPoILeR enables novel view synthesis of unconventional imaging modalities (infrared, polarimetric, multispectral) for scenes where only RGB images or very few multimodal samples are available, achieving accurate renderings without requiring expensive sensors.
Neural rendering techniques allow for accurate reconstruction of the geometry and color appearance of 3D scenes. Some methods have extended their use to additional imaging modalities, such as multispectral, infrared, or polarimetric data. However, all of these approaches require expensive sensors and calibrated setups to capture new multimodal frames for each new scene. We propose Spectral and Polarimetric Implicit Learned Representation (SPoILeR), a novel method to obtain multi-view consistent renderings of unconventional modalities for scenes where either only RGB frames or very few of the additional modalities are available. Thanks to a multimodal pre-training phase, the model learns the mutual correlation between different modalities. This step allows predicting accurate renderings of unconventional modalities during a fine-tuning phase supervised only by RGB images. Experimental results show that the approach can accurately render infrared, polarimetric, and multispectral frames for scenes where no input sample captured by these types of sensors is provided.