DiffusioNeRF: Regularizing Neural Radiance Fields with Denoising Diffusion Models

• URL: http://arxiv.org/abs/2302.12231v3
• Date: Wed, 8 Nov 2023 11:38:04 GMT
• Title: DiffusioNeRF: Regularizing Neural Radiance Fields with Denoising Diffusion Models
• Authors: Jamie Wynn, Daniyar Turmukhambetov
• Abstract summary: We learn a prior over scene geometry and color, using a denoising diffusion model (DDM) We show that, these gradients of logarithms of RGBD patch priors serve to regularize geometry and color of a scene. Evaluations on LLFF, the most relevant dataset, show that our learned prior achieves improved quality in the reconstructed geometry and improved to novel views.
• Score: 5.255302402546892
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Under good conditions, Neural Radiance Fields (NeRFs) have shown impressive results on novel view synthesis tasks. NeRFs learn a scene's color and density fields by minimizing the photometric discrepancy between training views and differentiable renderings of the scene. Once trained from a sufficient set of views, NeRFs can generate novel views from arbitrary camera positions. However, the scene geometry and color fields are severely under-constrained, which can lead to artifacts, especially when trained with few input views. To alleviate this problem we learn a prior over scene geometry and color, using a denoising diffusion model (DDM). Our DDM is trained on RGBD patches of the synthetic Hypersim dataset and can be used to predict the gradient of the logarithm of a joint probability distribution of color and depth patches. We show that, these gradients of logarithms of RGBD patch priors serve to regularize geometry and color of a scene. During NeRF training, random RGBD patches are rendered and the estimated gradient of the log-likelihood is backpropagated to the color and density fields. Evaluations on LLFF, the most relevant dataset, show that our learned prior achieves improved quality in the reconstructed geometry and improved generalization to novel views. Evaluations on DTU show improved reconstruction quality among NeRF methods.

Related papers

• Taming Latent Diffusion Model for Neural Radiance Field Inpainting [63.297262813285265]
  Neural Radiance Field (NeRF) is a representation for 3D reconstruction from multi-view images. We propose tempering the diffusion model'sity with per-scene customization and mitigating the textural shift with masked training. Our framework yields state-of-the-art NeRF inpainting results on various real-world scenes.
  arXiv  Detail & Related papers  (2024-04-15T17:59:57Z)
• CVT-xRF: Contrastive In-Voxel Transformer for 3D Consistent Radiance Fields from Sparse Inputs [65.80187860906115]
  We propose a novel approach to improve NeRF's performance with sparse inputs. We first adopt a voxel-based ray sampling strategy to ensure that the sampled rays intersect with a certain voxel in 3D space. We then randomly sample additional points within the voxel and apply a Transformer to infer the properties of other points on each ray, which are then incorporated into the volume rendering.
  arXiv  Detail & Related papers  (2024-03-25T15:56:17Z)
• GeCoNeRF: Few-shot Neural Radiance Fields via Geometric Consistency [31.22435282922934]
  We present a novel framework to regularize Neural Radiance Field (NeRF) in a few-shot setting with a geometry-aware consistency regularization. We show that our model achieves competitive results compared to state-of-the-art few-shot NeRF models.
  arXiv  Detail & Related papers  (2023-01-26T05:14:12Z)
• AligNeRF: High-Fidelity Neural Radiance Fields via Alignment-Aware Training [100.33713282611448]
  We conduct the first pilot study on training NeRF with high-resolution data. We propose the corresponding solutions, including marrying the multilayer perceptron with convolutional layers. Our approach is nearly free without introducing obvious training/testing costs.
  arXiv  Detail & Related papers  (2022-11-17T17:22:28Z)
• CLONeR: Camera-Lidar Fusion for Occupancy Grid-aided Neural Representations [77.90883737693325]
  This paper proposes CLONeR, which significantly improves upon NeRF by allowing it to model large outdoor driving scenes observed from sparse input sensor views. This is achieved by decoupling occupancy and color learning within the NeRF framework into separate Multi-Layer Perceptrons (MLPs) trained using LiDAR and camera data, respectively. In addition, this paper proposes a novel method to build differentiable 3D Occupancy Grid Maps (OGM) alongside the NeRF model, and leverage this occupancy grid for improved sampling of points along a ray for rendering in metric space.
  arXiv  Detail & Related papers  (2022-09-02T17:44:50Z)
• Mip-NeRF RGB-D: Depth Assisted Fast Neural Radiance Fields [0.696125353550498]
  Neural scene representations, such as neural radiance fields (NeRF), are based on training a multilayer perceptron (MLP) using a set of color images with known poses. An increasing number of devices now produce RGB-D information, which has been shown to be very important for a wide range of tasks. This paper investigates what improvements can be made to these promising implicit representations by incorporating depth information with the color images.
  arXiv  Detail & Related papers  (2022-05-19T07:11:42Z)
• 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)
• iNeRF: Inverting Neural Radiance Fields for Pose Estimation [68.91325516370013]
  We present iNeRF, a framework that performs mesh-free pose estimation by "inverting" a Neural RadianceField (NeRF) NeRFs have been shown to be remarkably effective for the task of view synthesis.
  arXiv  Detail & Related papers  (2020-12-10T18:36:40Z)

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.