Relighting Scenes with Object Insertions in Neural Radiance Fields

• URL: http://arxiv.org/abs/2406.14806v1
• Date: Fri, 21 Jun 2024 00:58:58 GMT
• Title: Relighting Scenes with Object Insertions in Neural Radiance Fields
• Authors: Xuening Zhu, Renjiao Yi, Xin Wen, Chenyang Zhu, Kai Xu,
• Abstract summary: We propose a novel NeRF-based pipeline for inserting object NeRFs into scene NeRFs. The proposed method achieves realistic relighting effects in extensive experimental evaluations.
• Score: 24.18050535794117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The insertion of objects into a scene and relighting are commonly utilized applications in augmented reality (AR). Previous methods focused on inserting virtual objects using CAD models or real objects from single-view images, resulting in highly limited AR application scenarios. We propose a novel NeRF-based pipeline for inserting object NeRFs into scene NeRFs, enabling novel view synthesis and realistic relighting, supporting physical interactions like casting shadows onto each other, from two sets of images depicting the object and scene. The lighting environment is in a hybrid representation of Spherical Harmonics and Spherical Gaussians, representing both high- and low-frequency lighting components very well, and supporting non-Lambertian surfaces. Specifically, we leverage the benefits of volume rendering and introduce an innovative approach for efficient shadow rendering by comparing the depth maps between the camera view and the light source view and generating vivid soft shadows. The proposed method achieves realistic relighting effects in extensive experimental evaluations.

Related papers

• Photorealistic Object Insertion with Diffusion-Guided Inverse Rendering [56.68286440268329]
  correct insertion of virtual objects in images of real-world scenes requires a deep understanding of the scene's lighting, geometry and materials. We propose using a personalized large diffusion model as guidance to a physically based inverse rendering process. Our method recovers scene lighting and tone-mapping parameters, allowing the photorealistic composition of arbitrary virtual objects in single frames or videos of indoor or outdoor scenes.
  arXiv  Detail & Related papers  (2024-08-19T05:15:45Z)
• Inverse Rendering of Glossy Objects via the Neural Plenoptic Function and Radiance Fields [45.64333510966844]
  Inverse rendering aims at recovering both geometry and materials of objects. We propose a novel 5D Neural Plenoptic Function (NeP) based on NeRFs and ray tracing. Our method can reconstruct high-fidelity geometry/materials of challenging glossy objects with complex lighting interactions from nearby objects.
  arXiv  Detail & Related papers  (2024-03-24T16:34:47Z)
• A Real-time Method for Inserting Virtual Objects into Neural Radiance Fields [38.370278809341954]
  We present the first real-time method for inserting a rigid virtual object into a neural radiance field. By exploiting the rich information about lighting and geometry in a NeRF, our method overcomes several challenges of object insertion in augmented reality.
  arXiv  Detail & Related papers  (2023-10-09T16:26:34Z)
• Spatiotemporally Consistent HDR Indoor Lighting Estimation [66.26786775252592]
  We propose a physically-motivated deep learning framework to solve the indoor lighting estimation problem. Given a single LDR image with a depth map, our method predicts spatially consistent lighting at any given image position. Our framework achieves photorealistic lighting prediction with higher quality compared to state-of-the-art single-image or video-based methods.
  arXiv  Detail & Related papers  (2023-05-07T20:36:29Z)
• Neural Fields meet Explicit Geometric Representation for Inverse Rendering of Urban Scenes [62.769186261245416]
  We present a novel inverse rendering framework for large urban scenes capable of jointly reconstructing the scene geometry, spatially-varying materials, and HDR lighting from a set of posed RGB images with optional depth. Specifically, we use a neural field to account for the primary rays, and use an explicit mesh (reconstructed from the underlying neural field) for modeling secondary rays that produce higher-order lighting effects such as cast shadows.
  arXiv  Detail & Related papers  (2023-04-06T17:51:54Z)
• Neural Light Field Estimation for Street Scenes with Differentiable Virtual Object Insertion [129.52943959497665]
  Existing works on outdoor lighting estimation typically simplify the scene lighting into an environment map. We propose a neural approach that estimates the 5D HDR light field from a single image. We show the benefits of our AR object insertion in an autonomous driving application.
  arXiv  Detail & Related papers  (2022-08-19T17:59:16Z)
• DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer [78.91753256634453]
  We consider the challenging problem of predicting intrinsic object properties from a single image by exploiting differentiables. In this work, we propose DIBR++, a hybrid differentiable which supports these effects by combining specularization and ray-tracing. Compared to more advanced physics-based differentiables, DIBR++ is highly performant due to its compact and expressive model.
  arXiv  Detail & Related papers  (2021-10-30T01:59:39Z)
• Object-Centric Neural Scene Rendering [19.687759175741824]
  We present a method for composing photorealistic scenes from captured images of objects. Our work builds upon neural radiance fields (NeRFs), which implicitly model the volumetric density and directionally-emitted radiance of a scene. We learn object-centric neural scattering functions (OSFs), a representation that models per-object light transport implicitly using a lighting- and view-dependent neural network.
  arXiv  Detail & Related papers  (2020-12-15T18:55:02Z)
• Neural Reflectance Fields for Appearance Acquisition [61.542001266380375]
  We present Neural Reflectance Fields, a novel deep scene representation that encodes volume density, normal and reflectance properties at any 3D point in a scene. We combine this representation with a physically-based differentiable ray marching framework that can render images from a neural reflectance field under any viewpoint and light.
  arXiv  Detail & Related papers  (2020-08-09T22:04:36Z)
• IllumiNet: Transferring Illumination from Planar Surfaces to Virtual Objects in Augmented Reality [38.83696624634213]
  This paper presents an illumination estimation method for virtual objects in real environment by learning. Given a single RGB image, our method directly infers the relit virtual object by transferring the illumination features extracted from planar surfaces in the scene to the desired geometries.
  arXiv  Detail & Related papers  (2020-07-12T13:11:14Z)

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.