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While current multi-frame restoration methods combine information frommultiple input images using 2D alignment techniques, recent advances in novelview synthesis are paving the way for a new paradigm relying on volumetricscene representations. In this work, we introduce the first 3D-basedmulti-frame denoising method that significantly outperforms its 2D-basedcounterparts with lower computational requirements. Our method extends themultiplane image (MPI) framework for novel view synthesis by introducing alearnable encoder-renderer pair manipulating multiplane representations infeature space. The encoder fuses information across views and operates in adepth-wise manner while the renderer fuses information across depths andoperates in a view-wise manner. The two modules are trained end-to-end andlearn to separate depths in an unsupervised way, giving rise to MultiplaneFeature (MPF) representations. Experiments on the Spaces and RealForward-Facing datasets as well as on raw burst data validate our approach forview synthesis, multi-frame denoising, and view synthesis under noisyconditions.

Efficient View Synthesis and 3D-based Multi-Frame Denoising with Multiplane Feature Representations