Spatial Annealing Smoothing for Efficient Few-shot Neural Rendering

• URL: http://arxiv.org/abs/2406.07828v1
• Date: Wed, 12 Jun 2024 02:48:52 GMT
• Title: Spatial Annealing Smoothing for Efficient Few-shot Neural Rendering
• Authors: Yuru Xiao, Xianming Liu, Deming Zhai, Kui Jiang, Junjun Jiang, Xiangyang Ji,
• Abstract summary: 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.
• Score: 106.0057551634008
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural Radiance Fields (NeRF) with hybrid representations have shown impressive capabilities in reconstructing scenes for view synthesis, delivering high efficiency. Nonetheless, their performance significantly drops with sparse view inputs, due to the issue of overfitting. While various regularization strategies have been devised to address these challenges, they often depend on inefficient assumptions or are not compatible with hybrid models. There is a clear need for a method that maintains efficiency and improves resilience to sparse views within a hybrid framework. In this paper, we introduce an accurate and efficient few-shot neural rendering method named Spatial Annealing smoothing regularized NeRF (SANeRF), which is specifically designed for a pre-filtering-driven hybrid representation architecture. We implement an exponential reduction of the sample space size from an initially large value. This methodology is crucial for stabilizing the early stages of the training phase and significantly contributes to the enhancement of the subsequent process of detail refinement. Our extensive experiments reveal that, by adding merely one line of code, SANeRF delivers superior rendering quality and much faster reconstruction speed compared to current few-shot NeRF methods. Notably, SANeRF outperforms FreeNeRF by 0.3 dB in PSNR on the Blender dataset, while achieving 700x faster reconstruction speed.

Related papers

• SGCNeRF: Few-Shot Neural Rendering via Sparse Geometric Consistency Guidance [106.0057551634008]
  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.
  arXiv  Detail & Related papers  (2024-04-01T08:37:57Z)
• ProNeRF: Learning Efficient Projection-Aware Ray Sampling for Fine-Grained Implicit Neural Radiance Fields [27.008124938806944]
  We propose ProNeRF, which provides an optimal trade-off between memory footprint (similar to NeRF), speed (faster than HyperReel), and quality (better than K-Planes) Our ProNeRF yields state-of-the-art metrics, being 15-23x faster with 0.65dB higher PSNR than NeRF and yielding 0.95dB higher PSNR than the best published sampler-based method, HyperReel.
  arXiv  Detail & Related papers  (2023-12-13T13:37:32Z)
• FRDiff : Feature Reuse for Universal Training-free Acceleration of Diffusion Models [16.940023904740585]
  We introduce an advanced acceleration technique that leverages the temporal redundancy inherent in diffusion models. Reusing feature maps with high temporal similarity opens up a new opportunity to save computation resources without compromising output quality.
  arXiv  Detail & Related papers  (2023-12-06T14:24:26Z)
• Hyb-NeRF: A Multiresolution Hybrid Encoding for Neural Radiance Fields [12.335934855851486]
  We present Hyb-NeRF, a novel neural radiance field with a multi-resolution hybrid encoding. We show that Hyb-NeRF achieves faster rendering speed with better rending quality and even a lower memory footprint in comparison to previous methods.
  arXiv  Detail & Related papers  (2023-11-21T10:01:08Z)
• VQ-NeRF: Vector Quantization Enhances Implicit Neural Representations [25.88881764546414]
  VQ-NeRF is an efficient pipeline for enhancing implicit neural representations via vector quantization. We present an innovative multi-scale NeRF sampling scheme that concurrently optimize the NeRF model at both compressed and original scales. We incorporate a semantic loss function to improve the geometric fidelity and semantic coherence of our 3D reconstructions.
  arXiv  Detail & Related papers  (2023-10-23T01:41:38Z)
• 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)
• MF-NeRF: Memory Efficient NeRF with Mixed-Feature Hash Table [62.164549651134465]
  We propose MF-NeRF, a memory-efficient NeRF framework that employs a Mixed-Feature hash table to improve memory efficiency and reduce training time while maintaining reconstruction quality. Our experiments with state-of-the-art Instant-NGP, TensoRF, and DVGO, indicate our MF-NeRF could achieve the fastest training time on the same GPU hardware with similar or even higher reconstruction quality.
  arXiv  Detail & Related papers  (2023-04-25T05:44:50Z)
• NeRFusion: Fusing Radiance Fields for Large-Scale Scene Reconstruction [50.54946139497575]
  We propose NeRFusion, a method that combines the advantages of NeRF and TSDF-based fusion techniques to achieve efficient large-scale reconstruction and photo-realistic rendering. We demonstrate that NeRFusion achieves state-of-the-art quality on both large-scale indoor and small-scale object scenes, with substantially faster reconstruction than NeRF and other recent methods.
  arXiv  Detail & Related papers  (2022-03-21T18:56:35Z)
• 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.