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Generalizable Patch-Based Neural Rendering

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

Neural rendering has received tremendous attention since the advent of Neural Radiance Fields (NeRF), and has pushed the state-of-the-art on novel-view synthesis considerably. The recent focus has been on models that overfit to a single scene, and the few attempts to learn models that can synthesize novel views of unseen scenes mostly consist of combining deep convolutional features with a NeRF-like model. We propose a different paradigm, where no deep visual features and no NeRF-like volume rendering are needed. Our method is capable of predicting the color of a target ray in a novel scene directly, just from a collection of patches sampled from the scene. We first leverage epipolar geometry to extract patches along the epipolar lines of each reference view. Each patch is linearly projected into a 1D feature vector and a sequence of transformers process the collection. For positional encoding, we parameterize rays as in a light field representation, with the crucial difference that the coordinates are canonicalized with respect to the target ray, which makes our method independent of the reference frame and improves generalization. We show that our approach outperforms the state-of-the-art on novel view synthesis of unseen scenes even when being trained with considerably less data than prior work. Our code is available at https://mohammedsuhail.net/gen_patch_neural_rendering/.

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Authors and Affiliations

  1. University of British Columbia, Vancouver, Canada

    Mohammed Suhail & Leonid Sigal

  2. Vector Institute for AI, Toronto, Canada

    Leonid Sigal

  3. Canada CIFAR AI Chair, Vancouver, Canada

    Leonid Sigal

  4. Google, Vancouver, Canada

    Carlos Esteves & Ameesh Makadia

Authors
  1. Mohammed Suhail
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  2. Carlos Esteves
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  3. Leonid Sigal
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  4. Ameesh Makadia
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Correspondence to Mohammed Suhail .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Suhail, M., Esteves, C., Sigal, L., Makadia, A. (2022). Generalizable Patch-Based Neural Rendering. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13692. Springer, Cham. https://doi.org/10.1007/978-3-031-19824-3_10

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