Generalizable Patch-Based Neural Rendering
- URL: http://arxiv.org/abs/2207.10662v1
- Date: Thu, 21 Jul 2022 17:57:04 GMT
- Title: Generalizable Patch-Based Neural Rendering
- Authors: Mohammed Suhail, Carlos Esteves, Leonid Sigal, Ameesh Makadia
- Abstract summary: We propose a new paradigm for learning models that can synthesize novel views of unseen scenes.
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 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.
- Score: 46.41746536545268
- License: http://creativecommons.org/licenses/by/4.0/
- 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 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.
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