Efficient View Synthesis and 3D-Based Multi-Frame Denoising With Multiplane Feature Representations

Thomas Tanay, AleŇ° Leonardis, Matteo Maggioni; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 20898-20907

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.

Related Material


[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Tanay_2023_CVPR, author = {Tanay, Thomas and Leonardis, Ale\v{s} and Maggioni, Matteo}, title = {Efficient View Synthesis and 3D-Based Multi-Frame Denoising With Multiplane Feature Representations}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2023}, pages = {20898-20907} }