Efficient View Synthesis and 3D-based Multi-Frame Denoising with Multiplane Feature Representations
This addresses the problem of efficient multi-frame denoising for computer vision applications, offering a novel approach that improves performance and reduces computational costs.
The paper tackled multi-frame denoising by introducing a 3D-based method using multiplane feature representations, which significantly outperformed 2D-based counterparts with lower computational requirements, as validated on datasets like Spaces and Real Forward-Facing.
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.