Is-NeRF: In-scattering Neural Radiance Field for Blurred Images

• URL: http://arxiv.org/abs/2508.13808v1
• Date: Tue, 19 Aug 2025 13:13:02 GMT
• Title: Is-NeRF: In-scattering Neural Radiance Field for Blurred Images
• Authors: Nan Luo, Chenglin Ye, Jiaxu Li, Gang Liu, Bo Wan, Di Wang, Lupeng Liu, Jun Xiao,
• Abstract summary: We propose a novel deblur neural radiance field, Is-NeRF, featuring explicit lightpath modeling in real-world environments.<n>We show that it effectively handles complex real-world scenarios, outperforming state-of-the-art approaches in generating high-fidelity images with accurate geometric details.
• Score: 14.665147888320595
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural Radiance Fields (NeRF) has gained significant attention for its prominent implicit 3D representation and realistic novel view synthesis capabilities. Available works unexceptionally employ straight-line volume rendering, which struggles to handle sophisticated lightpath scenarios and introduces geometric ambiguities during training, particularly evident when processing motion-blurred images. To address these challenges, this work proposes a novel deblur neural radiance field, Is-NeRF, featuring explicit lightpath modeling in real-world environments. By unifying six common light propagation phenomena through an in-scattering representation, we establish a new scattering-aware volume rendering pipeline adaptable to complex lightpaths. Additionally, we introduce an adaptive learning strategy that enables autonomous determining of scattering directions and sampling intervals to capture finer object details. The proposed network jointly optimizes NeRF parameters, scattering parameters, and camera motions to recover fine-grained scene representations from blurry images. Comprehensive evaluations demonstrate that it effectively handles complex real-world scenarios, outperforming state-of-the-art approaches in generating high-fidelity images with accurate geometric details.

Related papers

• Gear-NeRF: Free-Viewpoint Rendering and Tracking with Motion-aware Spatio-Temporal Sampling [70.34875558830241]
  We present a way for learning a-temporal (4D) embedding, based on semantic semantic gears to allow for stratified modeling of dynamic regions of rendering the scene. At the same time, almost for free, our tracking approach enables free-viewpoint of interest - a functionality not yet achieved by existing NeRF-based methods.
  arXiv  Detail & Related papers  (2024-06-06T03:37:39Z)
• NeRF On-the-go: Exploiting Uncertainty for Distractor-free NeRFs in the Wild [55.154625718222995]
  We introduce NeRF On-the-go, a simple yet effective approach that enables the robust synthesis of novel views in complex, in-the-wild scenes. Our method demonstrates a significant improvement over state-of-the-art techniques.
  arXiv  Detail & Related papers  (2024-05-29T02:53:40Z)
• NeRF-Casting: Improved View-Dependent Appearance with Consistent Reflections [57.63028964831785]
  Recent works have improved NeRF's ability to render detailed specular appearance of distant environment illumination, but are unable to synthesize consistent reflections of closer content. We address these issues with an approach based on ray tracing. Instead of querying an expensive neural network for the outgoing view-dependent radiance at points along each camera ray, our model casts rays from these points and traces them through the NeRF representation to render feature vectors.
  arXiv  Detail & Related papers  (2024-05-23T17:59:57Z)
• MS-NeRF: Multi-Space Neural Radiance Fields [48.0367339199913]
  Existing Neural Radiance Fields (NeRF) methods suffer from the existence of reflective objects, often resulting in blurry rendering.<n>We propose a multi-space neural radiance field (MS-NeRF) that represents the scene using a group of feature fields in parallel sub-spaces.<n>Our approach significantly outperforms the existing single-space NeRF methods for rendering high-quality scenes.
  arXiv  Detail & Related papers  (2023-05-07T13:11:07Z)
• TensoIR: Tensorial Inverse Rendering [51.57268311847087]
  TensoIR is a novel inverse rendering approach based on tensor factorization and neural fields. TensoRF is a state-of-the-art approach for radiance field modeling.
  arXiv  Detail & Related papers  (2023-04-24T21:39:13Z)
• Learning Neural Duplex Radiance Fields for Real-Time View Synthesis [33.54507228895688]
  We propose a novel approach to distill and bake NeRFs into highly efficient mesh-based neural representations. We demonstrate the effectiveness and superiority of our approach via extensive experiments on a range of standard datasets.
  arXiv  Detail & Related papers  (2023-04-20T17:59:52Z)
• NeAI: A Pre-convoluted Representation for Plug-and-Play Neural Ambient Illumination [28.433403714053103]
  We propose a framework named neural ambient illumination (NeAI) NeAI uses Neural Radiance Fields (NeRF) as a lighting model to handle complex lighting in a physically based way. Experiments demonstrate the superior performance of novel-view rendering compared to previous works.
  arXiv  Detail & Related papers  (2023-04-18T06:32:30Z)
• CLONeR: Camera-Lidar Fusion for Occupancy Grid-aided Neural Representations [77.90883737693325]
  This paper proposes CLONeR, which significantly improves upon NeRF by allowing it to model large outdoor driving scenes observed from sparse input sensor views. This is achieved by decoupling occupancy and color learning within the NeRF framework into separate Multi-Layer Perceptrons (MLPs) trained using LiDAR and camera data, respectively. In addition, this paper proposes a novel method to build differentiable 3D Occupancy Grid Maps (OGM) alongside the NeRF model, and leverage this occupancy grid for improved sampling of points along a ray for rendering in metric space.
  arXiv  Detail & Related papers  (2022-09-02T17:44:50Z)
• INFWIDE: Image and Feature Space Wiener Deconvolution Network for Non-blind Image Deblurring in Low-Light Conditions [32.35378513394865]
  We propose a novel non-blind deblurring method dubbed image and feature space Wiener deconvolution network (INFWIDE) INFWIDE removes noise and hallucinates saturated regions in the image space and suppresses ringing artifacts in the feature space. Experiments on synthetic data and real data demonstrate the superior performance of the proposed approach.
  arXiv  Detail & Related papers  (2022-07-17T15:22:31Z)
• Learning Neural Transmittance for Efficient Rendering of Reflectance Fields [43.24427791156121]
  We propose a novel method based on precomputed Neural Transmittance Functions to accelerate rendering of neural reflectance fields. Results on real and synthetic scenes demonstrate almost two order of magnitude speedup for renderings under environment maps with minimal accuracy loss.
  arXiv  Detail & Related papers  (2021-10-25T21:12:25Z)
• MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereo [52.329580781898116]
  We present MVSNeRF, a novel neural rendering approach that can efficiently reconstruct neural radiance fields for view synthesis. Unlike prior works on neural radiance fields that consider per-scene optimization on densely captured images, we propose a generic deep neural network that can reconstruct radiance fields from only three nearby input views via fast network inference.
  arXiv  Detail & Related papers  (2021-03-29T13:15:23Z)

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.