A Real-time Method for Inserting Virtual Objects into Neural Radiance Fields
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Abstract (arXiv)
We present the first real-time method for inserting a rigid virtual object into a neural radiance field, which produces realistic lighting and shadowing effects, as well as allows interactive manipulation of the object. By exploiting the rich information about lighting and geometry in a NeRF, our method overcomes several challenges of object insertion in augmented reality. For lighting estimation, we produce accurate, robust and 3D spatially-varying incident lighting that combines the near-field lighting from NeRF and an environment lighting to account for sources not covered by the NeRF. For occlusion, we blend the rendered virtual object with the background scene using an opacity map integrated from the NeRF. For shadows, with a precomputed field of spherical signed distance field, we query the visibility term for any point around the virtual object, and cast soft, detailed shadows onto 3D surfaces. Compared with state-of-the-art techniques, our approach can insert virtual object into scenes with superior fidelity, and has a great potential to be further applied to augmented reality systems.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 34 of its 58 references that are in the map. First posted on arXiv 9 Oct 2023.
Builds on
34 of 58 references.
References · 34
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- 2018Pyramid Stereo Matching Networkcited by 489
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22 more
- 2021Animatable Neural Radiance Fields for Modeling Dynamic Human Bodiescited by 361
- 2017End-to-End Learning of Geometry and Context for Deep Stereo Regressioncited by 360
- 2021PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relightingcited by 335
- 2021Learning Object-Compositional Neural Radiance Field for Editable Scene Renderingcited by 228
- 2019Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF from a Single Imagecited by 203
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- 2017Learning to Predict Indoor Illumination from a Single Imagecited by 161
- 2018High Quality Monocular Depth Estimation via Transfer Learningcited by 133
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- 2019Deep Parametric Indoor Lighting Estimationcited by 83
- 2021Learning Indoor Inverse Rendering with 3D Spatially-Varying Lightingcited by 83
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- 2022Real-Time Neural Light Field on Mobile Devicescited by 48
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- 2023Virtual Occlusions Through Implicit Depthcited by 5
Built on by
Citing papers per year: 2024: 2; 2025: 3.
5 papers.
- 2024MAIR++: Improving Multi-view Attention Inverse Rendering with Implicit Lighting Representationcited by 3
- 2025Diffusion Models are Secretly Zero-Shot 3DGS Harmonizerscited by 2
- 2025Scale-GS: Efficient Scalable Gaussian Splatting via Redundancy-filtering Training on Streaming Contentcited by 2
- 2025ComGS: Efficient 3D Object-Scene Composition via Surface Octahedral Probescited by 1
- 2024PanDORA: Casual HDR Radiance Acquisition of Indoor Scenes for Image-based Lightingcited by 0
- 2025ComGS: Efficient 3D Object-Scene Composition via Surface Octahedral Probes9 Oct 2025
- 2025Scale-GS: Efficient Scalable Gaussian Splatting via Redundancy-filtering Training on Streaming Content29 Aug 2025
- 2025Diffusion Models are Secretly Zero-Shot 3DGS Harmonizers9 Mar 2025
- 2024MAIR++: Improving Multi-view Attention Inverse Rendering with Implicit Lighting Representation13 Aug 2024
- 2024PanDORA: Casual HDR Radiance Acquisition of Indoor Scenes for Image-based Lighting8 Jul 2024