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Neural Implicit Field Editing Considering Object-environment Interaction

arXiv 2311.00425 ↗First posted 1 Nov 2023cs.CV cs.AIcited by 0

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Abstract (arXiv)

The 3D scene editing method based on neural implicit field has gained wide attention. It has achieved excellent results in 3D editing tasks. However, existing methods often blend the interaction between objects and scene environment. The change of scene appearance like shadows is failed to be displayed in the rendering view. In this paper, we propose an Object and Scene environment Interaction aware (OSI-aware) system, which is a novel two-stream neural rendering system considering object and scene environment interaction. To obtain illuminating conditions from the mixture soup, the system successfully separates the interaction between objects and scene environment by intrinsic decomposition method. To study the corresponding changes to the scene appearance from object-level editing tasks, we introduce a depth map guided scene inpainting method and shadow rendering method by point matching strategy. Extensive experiments demonstrate that our novel pipeline produce reasonable appearance changes in scene editing tasks. It also achieve competitive performance for the rendering quality in novel-view synthesis tasks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 28 of its 41 references that are in the map. First posted on arXiv 1 Nov 2023.

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28 of 41 references.

References · 28

  1. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  2. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
  3. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,037
  4. 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,701
  5. 2020Neural Sparse Voxel Fieldscited by 959
  6. 2019The Replica Dataset: A Digital Replica of Indoor Spacescited by 885
  7. 2019Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representationscited by 864
  8. 2020GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fieldscited by 784
  9. 2018Pixel2Mesh: Generating 3D Mesh Models from Single RGB Imagescited by 649
  10. 20163D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstructioncited by 587
  11. 2020NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesiscited by 493
  12. 2020NeRD: Neural Reflectance Decomposition from Image Collectionscited by 446
16 more
  1. 2021In-Place Scene Labelling and Understanding with Implicit Scene Representationcited by 411
  2. 2020Local Implicit Grid Representations for 3D Scenescited by 372
  3. 2021PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relightingcited by 332
  4. 2021Learning Object-Compositional Neural Radiance Field for Editable Scene Renderingcited by 228
  5. 2020Neural Reflectance Fields for Appearance Acquisitioncited by 196
  6. 2016Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervisioncited by 106
  7. 2018Multiresolution Tree Networks for 3D Point Cloud Processingcited by 91
  8. 2020Object-Centric Neural Scene Renderingcited by 90
  9. 2018Unsupervised Learning of Shape and Pose with Differentiable Point Cloudscited by 89
  10. 2019Rendering Synthetic Objects into Legacy Photographscited by 72
  11. 2019Neural Point Cloud Rendering via Multi-Plane Projectioncited by 60
  12. 2022IntrinsicNeRF: Learning Intrinsic Neural Radiance Fields for Editable Novel View Synthesiscited by 50
  13. 20183D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single Imagecited by 40
  14. 2020Learning to Factorize and Relight a Citycited by 36
  15. 2017Image2Mesh: A Learning Framework for Single Image 3D Reconstructioncited by 29
  16. 2018Joint Learning of Intrinsic Images and Semantic Segmentationcited by 26

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