IOVS4NeRF:Incremental Optimal View Selection for Large-Scale NeRFs

• URL: http://arxiv.org/abs/2407.18611v3
• Date: Mon, 17 Feb 2025 08:15:19 GMT
• Title: IOVS4NeRF:Incremental Optimal View Selection for Large-Scale NeRFs
• Authors: Jingpeng Xie, Shiyu Tan, Yuanlei Wang, Tianle Du, Yifei Xue, Yizhen Lao,
• Abstract summary: IOVS4NeRF is a framework that employs an uncertainty-guided incremental optimal view selection strategy to various NeRF implementations. Our experiments demonstrate that IOVS4NeRF achieves high-fidelity NeRF reconstruction with minimal computational resources.
• Score: 3.452532510087222
• License:
• Abstract: Large-scale Neural Radiance Fields (NeRF) reconstructions are typically hindered by the requirement for extensive image datasets and substantial computational resources. This paper introduces IOVS4NeRF, a framework that employs an uncertainty-guided incremental optimal view selection strategy adaptable to various NeRF implementations. Specifically, by leveraging a hybrid uncertainty model that combines rendering and positional uncertainties, the proposed method calculates the most informative view from among the candidates, thereby enabling incremental optimization of scene reconstruction. Our detailed experiments demonstrate that IOVS4NeRF achieves high-fidelity NeRF reconstruction with minimal computational resources, making it suitable for large-scale scene applications.

Related papers

• ASSR-NeRF: Arbitrary-Scale Super-Resolution on Voxel Grid for High-Quality Radiance Fields Reconstruction [27.21399221644529]
  NeRF-based methods reconstruct 3D scenes by building a radiance field with implicit or explicit representations. We propose Arbitrary-Scale Super-Resolution NeRF (ASSR-NeRF), a novel framework for super-resolution novel view synthesis (SRNVS)
  arXiv  Detail & Related papers  (2024-06-28T17:22:33Z)
• Matching Query Image Against Selected NeRF Feature for Efficient and Scalable Localization [6.0557193420601525]
  This work proposes MatLoc-NeRF, a novel matching-based localization framework using selected NeRF features. It addresses efficiency by employing a learnable feature selection mechanism that identifies informative NeRF features for matching with query images. Evaluations on public large-scale datasets demonstrate that MatLoc-NeRF achieves superior efficiency and accuracy compared to existing NeRF-based localization methods.
  arXiv  Detail & Related papers  (2024-06-17T17:29:21Z)
• ProvNeRF: Modeling per Point Provenance in NeRFs as a Stochastic Field [52.09661042881063]
  We propose an approach that models the bfprovenance for each point -- i.e., the locations where it is likely visible -- of NeRFs as a text field. We show that modeling per-point provenance during the NeRF optimization enriches the model with information on leading to improvements in novel view synthesis and uncertainty estimation.
  arXiv  Detail & Related papers  (2024-01-16T06:19:18Z)
• Informative Rays Selection for Few-Shot Neural Radiance Fields [0.3599866690398789]
  KeyNeRF is a simple yet effective method for training NeRF in few-shot scenarios by focusing on key informative rays. Our approach performs favorably against state-of-the-art methods, while requiring minimal changes to existing NeRFs.
  arXiv  Detail & Related papers  (2023-12-29T11:08:19Z)
• 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)
• Grid-guided Neural Radiance Fields for Large Urban Scenes [146.06368329445857]
  Recent approaches propose to geographically divide the scene and adopt multiple sub-NeRFs to model each region individually. An alternative solution is to use a feature grid representation, which is computationally efficient and can naturally scale to a large scene. We present a new framework that realizes high-fidelity rendering on large urban scenes while being computationally efficient.
  arXiv  Detail & Related papers  (2023-03-24T13:56:45Z)
• SiNeRF: Sinusoidal Neural Radiance Fields for Joint Pose Estimation and Scene Reconstruction [147.9379707578091]
  NeRFmm is the Neural Radiance Fields (NeRF) that deal with Joint Optimization tasks. Despite NeRFmm producing precise scene synthesis and pose estimations, it still struggles to outperform the full-annotated baseline on challenging scenes. We propose Sinusoidal Neural Radiance Fields (SiNeRF) that leverage sinusoidal activations for radiance mapping and a novel Mixed Region Sampling (MRS) for selecting ray batch efficiently.
  arXiv  Detail & Related papers  (2022-10-10T10:47:51Z)
• Distilled Low Rank Neural Radiance Field with Quantization for Light Field Compression [33.08737425706558]
  We propose a Quantized Distilled Low-Rank Neural Radiance Field (QDLR-NeRF) representation for the task of light field compression. Our proposed method learns an implicit scene representation in the form of a Neural Radiance Field (NeRF), which also enables view synthesis. Experimental results show that our proposed method yields better compression efficiency compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-07-30T08:19:29Z)
• NeRF-SR: High-Quality Neural Radiance Fields using Super-Sampling [82.99453001445478]
  We present NeRF-SR, a solution for high-resolution (HR) novel view synthesis with mostly low-resolution (LR) inputs. Our method is built upon Neural Radiance Fields (NeRF) that predicts per-point density and color with a multi-layer perceptron.
  arXiv  Detail & Related papers  (2021-12-03T07:33:47Z)
• Deep Adaptive Inference Networks for Single Image Super-Resolution [72.7304455761067]
  Single image super-resolution (SISR) has witnessed tremendous progress in recent years owing to the deployment of deep convolutional neural networks (CNNs) In this paper, we take a step forward to address this issue by leveraging the adaptive inference networks for deep SISR (AdaDSR) Our AdaDSR involves an SISR model as backbone and a lightweight adapter module which takes image features and resource constraint as input and predicts a map of local network depth.
  arXiv  Detail & Related papers  (2020-04-08T10:08:20Z)

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.