Low-Cost Neural Radiance Fields

• URL: http://arxiv.org/abs/2605.09312v1
• Date: Sun, 10 May 2026 04:13:45 GMT
• Title: Low-Cost Neural Radiance Fields
• Abstract summary: We present a comparative study of three accelerated NeRF variants - DS-NeRF, TensoRF, and HashNeRF.<n>We add a depth-supervision loss derived from COLMAP keypoints to TensoRF (TensoRF-DS) and evaluate it under reduced view counts.<n>We propose four architectural variants of the HashNeRF color and density networks, including residual and convolutional designs.
• Score: 0.7330284101822845
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural Radiance Fields (NeRF) achieve high-quality novel-view synthesis, but their long training times and reliance on dense input views limit accessibility. We present a comparative study of three accelerated NeRF variants - DS-NeRF, TensoRF, and HashNeRF and explore extensions targeted at the low-compute, low-data regime. First, we add a depth-supervision loss derived from COLMAP keypoints to TensoRF (TensoRF-DS) and evaluate it on the LLFF dataset under reduced view counts. Second, we ablate the feature-decoding MLP of TensoRF and study the effect of input downsampling on PSNR and runtime on the synthetic Lego scene. Third, we propose four architectural variants of the HashNeRF color and density networks, including residual and convolutional designs, and report PSNR/training-time tradeoffs under matched iteration budgets. Under iso-time evaluation, none of our extensions conclusively outperform the published baselines, but the experiments characterize which extensions transfer to constrained settings and surface design questions for future work.

Related papers

• $Δ$-NeRF: Incremental Refinement of Neural Radiance Fields through Residual Control and Knowledge Transfer [7.885378415708355]
  $$-NeRF is a unique modular residual framework for incremental NeRF refinement.<n>Experiments on satellite imagery demonstrate that $$-NeRF achieves performance comparable to joint training.
  arXiv  Detail & Related papers  (2025-11-25T19:46:15Z)
• 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)
• SGCNeRF: Few-Shot Neural Rendering via Sparse Geometric Consistency Guidance [136.15885067858298]
  This study presents a novel feature-matching-based sparse geometry regularization module, enhanced by a spatially consistent geometry filtering mechanism and a frequency-guided geometric regularization strategy.<n>Our experiments demonstrate that SGCNeRF achieves superior geometry-consistent outcomes and also surpasses FreeNeRF, with improvements of 0.7 dB in PSNR on LLFF and DTU.
  arXiv  Detail & Related papers  (2024-04-01T08:37:57Z)
• 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)
• Where and How: Mitigating Confusion in Neural Radiance Fields from Sparse Inputs [22.9859132310377]
  We present a novel learning framework, WaH-NeRF, which effectively mitigates confusion by tackling the following challenges. We propose a Semi-Supervised NeRF learning Paradigm based on pose perturbation and a Pixel-Patch Correspondence Loss to alleviate prediction confusion.
  arXiv  Detail & Related papers  (2023-08-05T15:59:15Z)
• 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)
• 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)
• 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)
• 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.