SSNeRF: Sparse View Semi-supervised Neural Radiance Fields with Augmentation

• URL: http://arxiv.org/abs/2408.09144v1
• Date: Sat, 17 Aug 2024 09:00:37 GMT
• Title: SSNeRF: Sparse View Semi-supervised Neural Radiance Fields with Augmentation
• Authors: Xiao Cao, Beibei Lin, Bo Wang, Zhiyong Huang, Robby T. Tan,
• Abstract summary: SSNeRF is a sparse view semi supervised NeRF method based on a teacher student framework. Our key idea is to challenge the NeRF module with progressively severe sparse view degradation. In this approach, the teacher NeRF generates novel views along with confidence scores, while the student NeRF, perturbed by the augmented input, learns from the high confidence pseudo labels.
• Score: 21.454340647455236
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sparse view NeRF is challenging because limited input images lead to an under constrained optimization problem for volume rendering. Existing methods address this issue by relying on supplementary information, such as depth maps. However, generating this supplementary information accurately remains problematic and often leads to NeRF producing images with undesired artifacts. To address these artifacts and enhance robustness, we propose SSNeRF, a sparse view semi supervised NeRF method based on a teacher student framework. Our key idea is to challenge the NeRF module with progressively severe sparse view degradation while providing high confidence pseudo labels. This approach helps the NeRF model become aware of noise and incomplete information associated with sparse views, thus improving its robustness. The novelty of SSNeRF lies in its sparse view specific augmentations and semi supervised learning mechanism. In this approach, the teacher NeRF generates novel views along with confidence scores, while the student NeRF, perturbed by the augmented input, learns from the high confidence pseudo labels. Our sparse view degradation augmentation progressively injects noise into volume rendering weights, perturbs feature maps in vulnerable layers, and simulates sparse view blurriness. These augmentation strategies force the student NeRF to recognize degradation and produce clearer rendered views. By transferring the student's parameters to the teacher, the teacher gains increased robustness in subsequent training iterations. Extensive experiments demonstrate the effectiveness of our SSNeRF in generating novel views with less sparse view degradation. We will release code upon acceptance.

Related papers

• Few-shot NeRF by Adaptive Rendering Loss Regularization [78.50710219013301]
  Novel view synthesis with sparse inputs poses great challenges to Neural Radiance Field (NeRF) Recent works demonstrate that the frequency regularization of Positional rendering can achieve promising results for few-shot NeRF. We propose Adaptive Rendering loss regularization for few-shot NeRF, dubbed AR-NeRF.
  arXiv  Detail & Related papers  (2024-10-23T13:05:26Z)
• Self-Evolving Neural Radiance Fields [31.124406548504794]
  We propose a novel framework, dubbed Self-Evolving Neural Radiance Fields (SE-NeRF), that applies a self-training framework to neural radiance field (NeRF) We formulate few-shot NeRF into a teacher-student framework to guide the network to learn a more robust representation of the scene. We show and evaluate that applying our self-training framework to existing models improves the quality of the rendered images and achieves state-of-the-art performance in multiple settings.
  arXiv  Detail & Related papers  (2023-12-02T02:28:07Z)
• Shielding the Unseen: Privacy Protection through Poisoning NeRF with Spatial Deformation [59.302770084115814]
  We introduce an innovative method of safeguarding user privacy against the generative capabilities of Neural Radiance Fields (NeRF) models. Our novel poisoning attack method induces changes to observed views that are imperceptible to the human eye, yet potent enough to disrupt NeRF's ability to accurately reconstruct a 3D scene. We extensively test our approach on two common NeRF benchmark datasets consisting of 29 real-world scenes with high-quality images.
  arXiv  Detail & Related papers  (2023-10-04T19:35:56Z)
• From NeRFLiX to NeRFLiX++: A General NeRF-Agnostic Restorer Paradigm [57.73868344064043]
  We propose NeRFLiX, a general NeRF-agnostic restorer paradigm that learns a degradation-driven inter-viewpoint mixer. We also present NeRFLiX++ with a stronger two-stage NeRF degradation simulator and a faster inter-viewpoint mixer. NeRFLiX++ is capable of restoring photo-realistic ultra-high-resolution outputs from noisy low-resolution NeRF-rendered views.
  arXiv  Detail & Related papers  (2023-06-10T09:19:19Z)
• NeRFVS: Neural Radiance Fields for Free View Synthesis via Geometry Scaffolds [60.1382112938132]
  We present NeRFVS, a novel neural radiance fields (NeRF) based method to enable free navigation in a room. NeRF achieves impressive performance in rendering images for novel views similar to the input views while suffering for novel views that are significantly different from the training views.
  arXiv  Detail & Related papers  (2023-04-13T06:40:08Z)
• Self-NeRF: A Self-Training Pipeline for Few-Shot Neural Radiance Fields [17.725937326348994]
  We propose Self-NeRF, a self-evolved NeRF that iteratively refines the radiance fields with very few number of input views. In each iteration, we label unseen views with the predicted colors or warped pixels generated by the model from the preceding iteration. These expanded pseudo-views are afflicted by imprecision in color and warping artifacts, which degrades the performance of NeRF.
  arXiv  Detail & Related papers  (2023-03-10T08:22:36Z)
• StegaNeRF: Embedding Invisible Information within Neural Radiance Fields [61.653702733061785]
  We present StegaNeRF, a method for steganographic information embedding in NeRF renderings. We design an optimization framework allowing accurate hidden information extractions from images rendered by NeRF. StegaNeRF signifies an initial exploration into the novel problem of instilling customizable, imperceptible, and recoverable information to NeRF renderings.
  arXiv  Detail & Related papers  (2022-12-03T12:14:19Z)
• PANeRF: Pseudo-view Augmentation for Improved Neural Radiance Fields Based on Few-shot Inputs [3.818285175392197]
  neural radiance fields (NeRF) have promising applications for novel views of complex scenes. NeRF requires dense input views, typically numbering in the hundreds, for generating high-quality images. We propose pseudo-view augmentation of NeRF, a scheme that expands a sufficient amount of data by considering the geometry of few-shot inputs.
  arXiv  Detail & Related papers  (2022-11-23T08:01:10Z)
• ActiveNeRF: Learning where to See with Uncertainty Estimation [36.209200774203005]
  Recently, Neural Radiance Fields (NeRF) has shown promising performances on reconstructing 3D scenes and synthesizing novel views from a sparse set of 2D images. We present a novel learning framework, ActiveNeRF, aiming to model a 3D scene with a constrained input budget.
  arXiv  Detail & Related papers  (2022-09-18T12:09:15Z)
• Aug-NeRF: Training Stronger Neural Radiance Fields with Triple-Level Physically-Grounded Augmentations [111.08941206369508]
  We propose Augmented NeRF (Aug-NeRF), which for the first time brings the power of robust data augmentations into regularizing the NeRF training. Our proposal learns to seamlessly blend worst-case perturbations into three distinct levels of the NeRF pipeline. Aug-NeRF effectively boosts NeRF performance in both novel view synthesis and underlying geometry reconstruction.
  arXiv  Detail & Related papers  (2022-07-04T02:27:07Z)

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.