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ViewFormer: NeRF-Free Neural Rendering from Few Images Using Transformers

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

Novel view synthesis is a long-standing problem. In this work, we consider a variant of the problem where we are given only a few context views sparsely covering a scene or an object. The goal is to predict novel viewpoints in the scene, which requires learning priors. The current state of the art is based on Neural Radiance Field (NeRF), and while achieving impressive results, the methods suffer from long training times as they require evaluating millions of 3D point samples via a neural network for each image. We propose a 2D-only method that maps multiple context views and a query pose to a new image in a single pass of a neural network. Our model uses a two-stage architecture consisting of a codebook and a transformer model. The codebook is used to embed individual images into a smaller latent space, and the transformer solves the view synthesis task in this more compact space. To train our model efficiently, we introduce a novel branching attention mechanism that allows us to use the same model not only for neural rendering but also for camera pose estimation. Experimental results on real-world scenes show that our approach is competitive compared to NeRF-based methods while not reasoning explicitly in 3D, and it is faster to train.

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Notes

  1. 1.

    We tried dynamic weighting as described in [29], but it performed worse.

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Acknowledgement

This work was supported by the European Regional Development Fund under projects IMPACT (reg. no. CZ.02.1.01/0.0/0.0/15_003/0000468) and Robotics for Industry 4.0 (reg. no. CZ.02.1.01/0.0/0.0/15_003/0000470), the EU Horizon 2020 project RICAIP (grant agreement No 857306), the Grant Agency of the Czech Technical University in Prague (grant no. SGS22/112/OHK3/2T/13), and the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:90140).

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Authors and Affiliations

  1. Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic

    Jonáš Kulhánek, Erik Derner, Torsten Sattler & Robert Babuška

  2. Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic

    Jonáš Kulhánek

  3. Cognitive Robotics, Faculty of 3mE, Delft University of Technology, Delft, The Netherlands

    Robert Babuška

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  1. Jonáš Kulhánek
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Correspondence to Jonáš Kulhánek .

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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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Kulhánek, J., Derner, E., Sattler, T., Babuška, R. (2022). ViewFormer: NeRF-Free Neural Rendering from Few Images Using Transformers. 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 13675. Springer, Cham. https://doi.org/10.1007/978-3-031-19784-0_12

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