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.

Related papers

• Learning to Render Novel Views from Wide-Baseline Stereo Pairs [26.528667940013598]
  We introduce a method for novel view synthesis given only a single wide-baseline stereo image pair. Existing approaches to novel view synthesis from sparse observations fail due to recovering incorrect 3D geometry. We propose an efficient, image-space epipolar line sampling scheme to assemble image features for a target ray.
  arXiv  Detail & Related papers  (2023-04-17T17:40:52Z)
• Cascaded and Generalizable Neural Radiance Fields for Fast View Synthesis [35.035125537722514]
  We present CG-NeRF, a cascade and generalizable neural radiance fields method for view synthesis. We first train CG-NeRF on multiple 3D scenes of the DTU dataset. We show that CG-NeRF outperforms state-of-the-art generalizable neural rendering methods on various synthetic and real datasets.
  arXiv  Detail & Related papers  (2022-08-09T12:23:48Z)
• Vision Transformer for NeRF-Based View Synthesis from a Single Input Image [49.956005709863355]
  We propose to leverage both the global and local features to form an expressive 3D representation. To synthesize a novel view, we train a multilayer perceptron (MLP) network conditioned on the learned 3D representation to perform volume rendering. Our method can render novel views from only a single input image and generalize across multiple object categories using a single model.
  arXiv  Detail & Related papers  (2022-07-12T17:52:04Z)
• InfoNeRF: Ray Entropy Minimization for Few-Shot Neural Volume Rendering [55.70938412352287]
  We present an information-theoretic regularization technique for few-shot novel view synthesis based on neural implicit representation. The proposed approach minimizes potential reconstruction inconsistency that happens due to insufficient viewpoints. We achieve consistently improved performance compared to existing neural view synthesis methods by large margins on multiple standard benchmarks.
  arXiv  Detail & Related papers  (2021-12-31T11:56:01Z)
• RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs [79.00855490550367]
  We show that NeRF can produce photorealistic renderings of unseen viewpoints when many input views are available. We address this by regularizing the geometry and appearance of patches rendered from unobserved viewpoints. Our model outperforms not only other methods that optimize over a single scene, but also conditional models that are extensively pre-trained on large multi-view datasets.
  arXiv  Detail & Related papers  (2021-12-01T18:59:46Z)
• GeoNeRF: Generalizing NeRF with Geometry Priors [2.578242050187029]
  We present GeoNeRF, a generalizable photorealistic novel view method based on neural radiance fields. Our approach consists of two main stages: a geometry reasoner and a synthesis. Experiments show that GeoNeRF outperforms state-of-the-art generalizable neural rendering models on various synthetic and real datasets.
  arXiv  Detail & Related papers  (2021-11-26T15:15:37Z)
• NeLF: Practical Novel View Synthesis with Neural Light Field [93.41020940730915]
  We present a practical and robust deep learning solution for the novel view synthesis of complex scenes. In our approach, a continuous scene is represented as a light field, i.e., a set of rays, each of which has a corresponding color. Our method achieves state-of-the-art novel view synthesis results while maintaining an interactive frame rate.
  arXiv  Detail & Related papers  (2021-05-15T01:20:30Z)
• NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis [78.5281048849446]
  We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes. Our algorithm represents a scene using a fully-connected (non-convolutional) deep network. Because volume rendering is naturally differentiable, the only input required to optimize our representation is a set of images with known camera poses.
  arXiv  Detail & Related papers  (2020-03-19T17:57:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.