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

• URL: http://arxiv.org/abs/2303.18139v2
• Date: Wed, 5 Apr 2023 11:08:37 GMT
• Title: Efficient View Synthesis and 3D-based Multi-Frame Denoising with Multiplane Feature Representations
• Authors: Thomas Tanay and Ale\v{s} Leonardis and Matteo Maggioni
• Abstract summary: We introduce the first 3D-based multi-frame denoising method that significantly outperforms its 2D-based counterparts with lower computational requirements. Our method extends the multiplane image (MPI) framework for novel view synthesis by introducing a learnable encoder-renderer pair manipulating multiplane in feature space.
• Score: 1.18885605647513
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While current multi-frame restoration methods combine information from multiple input images using 2D alignment techniques, recent advances in novel view synthesis are paving the way for a new paradigm relying on volumetric scene representations. In this work, we introduce the first 3D-based multi-frame denoising method that significantly outperforms its 2D-based counterparts with lower computational requirements. Our method extends the multiplane image (MPI) framework for novel view synthesis by introducing a learnable encoder-renderer pair manipulating multiplane representations in feature space. The encoder fuses information across views and operates in a depth-wise manner while the renderer fuses information across depths and operates in a view-wise manner. The two modules are trained end-to-end and learn to separate depths in an unsupervised way, giving rise to Multiplane Feature (MPF) representations. Experiments on the Spaces and Real Forward-Facing datasets as well as on raw burst data validate our approach for view synthesis, multi-frame denoising, and view synthesis under noisy conditions.

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