NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

• URL: http://arxiv.org/abs/2003.08934v2
• Date: Mon, 3 Aug 2020 22:17:31 GMT
• Title: NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
• Authors: Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng
• Abstract summary: 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.
• Score: 78.5281048849446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully-connected (non-convolutional) deep network, whose input is a single continuous 5D coordinate (spatial location $(x,y,z)$ and viewing direction $(\theta, \phi)$) and whose output is the volume density and view-dependent emitted radiance at that spatial location. We synthesize views by querying 5D coordinates along camera rays and use classic volume rendering techniques to project the output colors and densities into an image. Because volume rendering is naturally differentiable, the only input required to optimize our representation is a set of images with known camera poses. We describe how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis. View synthesis results are best viewed as videos, so we urge readers to view our supplementary video for convincing comparisons.

Related papers

• Sampling for View Synthesis: From Local Light Field Fusion to Neural Radiance Fields and Beyond [27.339452004523082]
  Local light field fusion proposes an algorithm for practical view synthesis from an irregular grid of sampled views. We achieve the perceptual quality of Nyquist rate view sampling while using up to 4000x fewer views. We reprise some of the recent results on sparse and even single image view synthesis.
  arXiv  Detail & Related papers  (2024-08-08T16:56:03Z)
• Multi-Plane Neural Radiance Fields for Novel View Synthesis [5.478764356647437]
  Novel view synthesis is a long-standing problem that revolves around rendering frames of scenes from novel camera viewpoints. In this work, we examine the performance, generalization, and efficiency of single-view multi-plane neural radiance fields. We propose a new multiplane NeRF architecture that accepts multiple views to improve the synthesis results and expand the viewing range.
  arXiv  Detail & Related papers  (2023-03-03T06:32:55Z)
• Remote Sensing Novel View Synthesis with Implicit Multiplane Representations [26.33490094119609]
  We propose a novel remote sensing view synthesis method by leveraging the recent advances in implicit neural representations. Considering the overhead and far depth imaging of remote sensing images, we represent the 3D space by combining implicit multiplane images (MPI) representation and deep neural networks. Images from any novel views can be freely rendered on the basis of the reconstructed model.
  arXiv  Detail & Related papers  (2022-05-18T13:03:55Z)
• 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)
• IBRNet: Learning Multi-View Image-Based Rendering [67.15887251196894]
  We present a method that synthesizes novel views of complex scenes by interpolating a sparse set of nearby views. By drawing on source views at render time, our method hearkens back to classic work on image-based rendering.
  arXiv  Detail & Related papers  (2021-02-25T18:56:21Z)
• Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video [76.19076002661157]
  Non-Rigid Neural Radiance Fields (NR-NeRF) is a reconstruction and novel view synthesis approach for general non-rigid dynamic scenes. We show that even a single consumer-grade camera is sufficient to synthesize sophisticated renderings of a dynamic scene from novel virtual camera views.
  arXiv  Detail & Related papers  (2020-12-22T18:46:12Z)
• Neural Radiance Flow for 4D View Synthesis and Video Processing [59.9116932930108]
  We present a method to learn a 4D spatial-temporal representation of a dynamic scene from a set of RGB images. Key to our approach is the use of a neural implicit representation that learns to capture the 3D occupancy, radiance, and dynamics of the scene.
  arXiv  Detail & Related papers  (2020-12-17T17:54:32Z)
• Free View Synthesis [100.86844680362196]
  We present a method for novel view synthesis from input images that are freely distributed around a scene. Our method does not rely on a regular arrangement of input views, can synthesize images for free camera movement through the scene, and works for general scenes with unconstrained geometric layouts.
  arXiv  Detail & Related papers  (2020-08-12T18:16:08Z)

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.