SGCNeRF: Few-Shot Neural Rendering via Sparse Geometric Consistency Guidance

• URL: http://arxiv.org/abs/2404.00992v2
• Date: Mon, 17 Jun 2024 08:16:38 GMT
• Title: SGCNeRF: Few-Shot Neural Rendering via Sparse Geometric Consistency Guidance
• Authors: Yuru Xiao, Xianming Liu, Deming Zhai, Kui Jiang, Junjun Jiang, Xiangyang Ji,
• Abstract summary: FreeNeRF attempts to overcome this limitation by integrating implicit geometry regularization. New study introduces a novel feature matching based sparse geometry regularization module. module excels in pinpointing high-frequency keypoints, thereby safeguarding the integrity of fine details.
• Score: 106.0057551634008
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural Radiance Field (NeRF) technology has made significant strides in creating novel viewpoints. However, its effectiveness is hampered when working with sparsely available views, often leading to performance dips due to overfitting. FreeNeRF attempts to overcome this limitation by integrating implicit geometry regularization, which incrementally improves both geometry and textures. Nonetheless, an initial low positional encoding bandwidth results in the exclusion of high-frequency elements. The quest for a holistic approach that simultaneously addresses overfitting and the preservation of high-frequency details remains ongoing. This study introduces a novel feature matching based sparse geometry regularization module. This module excels in pinpointing high-frequency keypoints, thereby safeguarding the integrity of fine details. Through progressive refinement of geometry and textures across NeRF iterations, we unveil an effective few-shot neural rendering architecture, designated as SGCNeRF, for enhanced novel view synthesis. Our experiments demonstrate that SGCNeRF not only achieves superior geometry-consistent outcomes but also surpasses FreeNeRF, with improvements of 0.7 dB and 0.6 dB in PSNR on the LLFF and DTU datasets, respectively.

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)
• GeoNLF: Geometry guided Pose-Free Neural LiDAR Fields [10.753993328978542]
  We propose a hybrid framework performing alternately global neural reconstruction and pure geometric pose optimization. Experiments on NuScenes and KITTI-360 datasets demonstrate the superiority of GeoNLF in both novel view synthesis and multi-view registration.
  arXiv  Detail & Related papers  (2024-07-08T04:19:49Z)
• Spatial Annealing Smoothing for Efficient Few-shot Neural Rendering [106.0057551634008]
  We introduce an accurate and efficient few-shot neural rendering method named Spatial Annealing smoothing regularized NeRF (SANeRF) By adding merely one line of code, SANeRF delivers superior rendering quality and much faster reconstruction speed compared to current few-shot NeRF methods.
  arXiv  Detail & Related papers  (2024-06-12T02:48:52Z)
• CombiNeRF: A Combination of Regularization Techniques for Few-Shot Neural Radiance Field View Synthesis [1.374796982212312]
  Neural Radiance Fields (NeRFs) have shown impressive results for novel view synthesis when a sufficiently large amount of views are available. We propose CombiNeRF, a framework that synergically combines several regularization techniques, some of them novel, in order to unify the benefits of each. We show that CombiNeRF outperforms the state-of-the-art methods with few-shot settings in several publicly available datasets.
  arXiv  Detail & Related papers  (2024-03-21T13:59:00Z)
• NeRF-VPT: Learning Novel View Representations with Neural Radiance Fields via View Prompt Tuning [63.39461847093663]
  We propose NeRF-VPT, an innovative method for novel view synthesis to address these challenges. Our proposed NeRF-VPT employs a cascading view prompt tuning paradigm, wherein RGB information gained from preceding rendering outcomes serves as instructive visual prompts for subsequent rendering stages. NeRF-VPT only requires sampling RGB data from previous stage renderings as priors at each training stage, without relying on extra guidance or complex techniques.
  arXiv  Detail & Related papers  (2024-03-02T22:08:10Z)
• PNeRFLoc: Visual Localization with Point-based Neural Radiance Fields [54.8553158441296]
  We propose a novel visual localization framework, ie, PNeRFLoc, based on a unified point-based representation. On the one hand, PNeRFLoc supports the initial pose estimation by matching 2D and 3D feature points. On the other hand, it also enables pose refinement with novel view synthesis using rendering-based optimization.
  arXiv  Detail & Related papers  (2023-12-17T08:30:00Z)
• GARF:Geometry-Aware Generalized Neural Radiance Field [47.76524984421343]
  We propose Geometry-Aware Generalized Neural Radiance Field (GARF) with a geometry-aware dynamic sampling (GADS) strategy. Our framework infers the unseen scenes on both pixel-scale and geometry-scale with only a few input images. Experiments on indoor and outdoor datasets show that GARF reduces samples by more than 25%, while improving rendering quality and 3D geometry estimation.
  arXiv  Detail & Related papers  (2022-12-05T14:00:59Z)
• 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)

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.