mlsanityData to 7 Oct 2026

Intrinsic3D: High-Quality 3D Reconstruction by Joint Appearance and Geometry Optimization with Spatially-Varying Lighting

arXiv 1708.01670 ↗First posted 4 Aug 2017cs.CVcited by 31

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Abstract (arXiv)

We introduce a novel method to obtain high-quality 3D reconstructions from consumer RGB-D sensors. Our core idea is to simultaneously optimize for geometry encoded in a signed distance field (SDF), textures from automatically-selected keyframes, and their camera poses along with material and scene lighting. To this end, we propose a joint surface reconstruction approach that is based on Shape-from-Shading (SfS) techniques and utilizes the estimation of spatially-varying spherical harmonics (SVSH) from subvolumes of the reconstructed scene. Through extensive examples and evaluations, we demonstrate that our method dramatically increases the level of detail in the reconstructed scene geometry and contributes highly to consistent surface texture recovery.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Aug 2017.

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Citing papers per year: 2021: 9; 2022: 6; 2023: 9; 2024: 3; 2025: 3; 2026: 1.

31 papers.

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  1. 2021PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relightingcited by 332
  2. 2022AKB-48: A Real-World Articulated Object Knowledge Basecited by 81
  3. 2021Unified Shape and SVBRDF Recovery using Differentiable Monte Carlo Renderingcited by 71
  4. 2022StyleLight: HDR Panorama Generation for Lighting Estimation and Editingcited by 59
  5. 2021Deep Implicit Surface Point Prediction Networkscited by 40
  6. 2023DiffComplete: Diffusion-based Generative 3D Shape Completioncited by 35
  7. 2023NeMF: Inverse Volume Rendering with Neural Microflake Fieldcited by 34
  8. 2024Neural Material Adaptor for Visual Grounding of Intrinsic Dynamicscited by 30
  9. 2021GMLight: Lighting Estimation via Geometric Distribution Approximationcited by 28
  10. 2021Neural Radiance Fields Approach to Deep Multi-View Photometric Stereocited by 25
  11. 2023Factorized Inverse Path Tracing for Efficient and Accurate Material-Lighting Estimationcited by 22
  12. 2023WildLight: In-the-wild Inverse Rendering with a Flashlightcited by 20
19 more
  1. 2021Sparse Needlets for Lighting Estimation with Spherical Transport Losscited by 18
  2. 2023MAIR: Multi-view Attention Inverse Rendering with 3D Spatially-Varying Lighting Estimationcited by 12
  3. 2021ASH: A Modern Framework for Parallel Spatial Hashing in 3D Perceptioncited by 11
  4. 2022Deep Graph Learning for Spatially-Varying Indoor Lighting Predictioncited by 6
  5. 2022Free-form 3D Scene Inpainting with Dual-stream GANcited by 6
  6. 2022Adaptive Joint Optimization for 3D Reconstruction with Differentiable Renderingcited by 5
  7. 2022High-Quality RGB-D Reconstruction via Multi-View Uncalibrated Photometric Stereo and Gradient-SDFcited by 5
  8. 2023TwinTex: Geometry-aware Texture Generation for Abstracted 3D Architectural Modelscited by 5
  9. 2021One Ring to Rule Them All: a simple solution to multi-view 3D-Reconstruction of shapes with unknown BRDF via a small Recurrent ResNetcited by 3
  10. 2023Dynamic Voxel Grid Optimization for High-Fidelity RGB-D Supervised Surface Reconstructioncited by 3
  11. 2023Towards Scalable Multi-View Reconstruction of Geometry and Materialscited by 3
  12. 2024MAIR++: Improving Multi-view Attention Inverse Rendering with Implicit Lighting Representationcited by 3
  13. 2021Spatially and color consistent environment lighting estimation using deep neural networks for mixed realitycited by 1
  14. 2023LitAR: Visually Coherent Lighting for Mobile Augmented Realitycited by 1
  15. 2024Spin-UP: Spin Light for Natural Light Uncalibrated Photometric Stereocited by 1
  16. 2025Aerial Path Planning for Urban Geometry and Texture Co-Capturecited by 1
  17. 2025VQT-Light:Lightweight HDR Illumination Map Prediction with Richer Texture.pdfcited by 0
  18. 2025DiffTex: Differentiable Texturing for Architectural Proxy Modelscited by 0
  19. 2026RehearsalNeRF: Decoupling Intrinsic Neural Fields of Dynamic Illuminations for Scene Editingcited by 0