AdaNeRF: Adaptive Sampling for Real-time Rendering of Neural Radiance Fields

• URL: http://arxiv.org/abs/2207.10312v1
• Date: Thu, 21 Jul 2022 05:59:13 GMT
• Title: AdaNeRF: Adaptive Sampling for Real-time Rendering of Neural Radiance Fields
• Authors: Andreas Kurz, Thomas Neff, Zhaoyang Lv, Michael Zollh\"ofer, Markus Steinberger
• Abstract summary: Novel view synthesis has recently been revolutionized by learning neural radiance fields directly from sparse observations. rendering images with this new paradigm is slow due to the fact that an accurate quadrature of the volume rendering equation requires a large number of samples for each ray. We propose a novel dual-network architecture that takes an direction by learning how to best reduce the number of required sample points.
• Score: 8.214695794896127
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Novel view synthesis has recently been revolutionized by learning neural radiance fields directly from sparse observations. However, rendering images with this new paradigm is slow due to the fact that an accurate quadrature of the volume rendering equation requires a large number of samples for each ray. Previous work has mainly focused on speeding up the network evaluations that are associated with each sample point, e.g., via caching of radiance values into explicit spatial data structures, but this comes at the expense of model compactness. In this paper, we propose a novel dual-network architecture that takes an orthogonal direction by learning how to best reduce the number of required sample points. To this end, we split our network into a sampling and shading network that are jointly trained. Our training scheme employs fixed sample positions along each ray, and incrementally introduces sparsity throughout training to achieve high quality even at low sample counts. After fine-tuning with the target number of samples, the resulting compact neural representation can be rendered in real-time. Our experiments demonstrate that our approach outperforms concurrent compact neural representations in terms of quality and frame rate and performs on par with highly efficient hybrid representations. Code and supplementary material is available at https://thomasneff.github.io/adanerf.

Related papers

• Efficient NeRF Optimization -- Not All Samples Remain Equally Hard [9.404889815088161]
  We propose an application of online hard sample mining for efficient training of Neural Radiance Fields (NeRF) NeRF models produce state-of-the-art quality for many 3D reconstruction and rendering tasks but require substantial computational resources.
  arXiv  Detail & Related papers  (2024-08-06T13:49:01Z)
• Adaptive Shells for Efficient Neural Radiance Field Rendering [92.18962730460842]
  We propose a neural radiance formulation that smoothly transitions between- and surface-based rendering. Our approach enables efficient rendering at very high fidelity. We also demonstrate that the extracted envelope enables downstream applications such as animation and simulation.
  arXiv  Detail & Related papers  (2023-11-16T18:58:55Z)
• RL-based Stateful Neural Adaptive Sampling and Denoising for Real-Time Path Tracing [1.534667887016089]
  MonteCarlo path tracing is a powerful technique for realistic image synthesis but suffers from high levels of noise at low sample counts. We propose a framework with end-to-end training of a sampling importance network, a latent space encoder network, and a denoiser network.
  arXiv  Detail & Related papers  (2023-10-05T12:39:27Z)
• Volume Feature Rendering for Fast Neural Radiance Field Reconstruction [11.05302598034426]
  Neural radiance fields (NeRFs) are able to synthesize realistic novel views from multi-view images captured from distinct positions and perspectives. In NeRF's rendering pipeline, neural networks are used to represent a scene independently or transform queried learnable feature vector of a point to the expected color or density. We propose to render the queried feature vectors of a ray first and then transform the rendered feature vector to the final pixel color by a neural network.
  arXiv  Detail & Related papers  (2023-05-29T06:58:27Z)
• Neural Adaptive SCEne Tracing [24.781844909539686]
  We present NAScenT, the first neural rendering method based on directly training a hybrid explicit-implicit neural representation. NAScenT is capable of reconstructing challenging scenes including both large, sparsely populated volumes like UAV captured outdoor environments.
  arXiv  Detail & Related papers  (2022-02-28T10:27:23Z)
• 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)
• Learning Neural Light Fields with Ray-Space Embedding Networks [51.88457861982689]
  We propose a novel neural light field representation that is compact and directly predicts integrated radiance along rays. Our method achieves state-of-the-art quality on dense forward-facing datasets such as the Stanford Light Field dataset.
  arXiv  Detail & Related papers  (2021-12-02T18:59:51Z)
• NeuSample: Neural Sample Field for Efficient View Synthesis [129.10351459066501]
  We propose a lightweight module which names a neural sample field. The proposed sample field maps rays into sample distributions, which can be transformed into point coordinates and fed into radiance fields for volume rendering. We show that NeuSample achieves better rendering quality than NeRF while enjoying a faster inference speed.
  arXiv  Detail & Related papers  (2021-11-30T16:43:49Z)
• NeRF in detail: Learning to sample for view synthesis [104.75126790300735]
  Neural radiance fields (NeRF) methods have demonstrated impressive novel view synthesis. In this work we address a clear limitation of the vanilla coarse-to-fine approach -- that it is based on a performance and not trained end-to-end for the task at hand. We introduce a differentiable module that learns to propose samples and their importance for the fine network, and consider and compare multiple alternatives for its neural architecture.
  arXiv  Detail & Related papers  (2021-06-09T17:59:10Z)
• Neural BRDF Representation and Importance Sampling [79.84316447473873]
  We present a compact neural network-based representation of reflectance BRDF data. We encode BRDFs as lightweight networks, and propose a training scheme with adaptive angular sampling. We evaluate encoding results on isotropic and anisotropic BRDFs from multiple real-world datasets.
  arXiv  Detail & Related papers  (2021-02-11T12:00:24Z)

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.