mlsanityData to 14 Oct 2026

LightOctree: Lightweight 3D Spatially-Coherent Indoor Lighting Estimation

arXiv 2404.03925 ↗First posted 5 Apr 2024cs.CVcited by 4

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Abstract (arXiv)

We present a lightweight solution for estimating spatially-coherent indoor lighting from a single RGB image. Previous methods for estimating illumination using volumetric representations have overlooked the sparse distribution of light sources in space, necessitating substantial memory and computational resources for achieving high-quality results. We introduce a unified, voxel octree-based illumination estimation framework to produce 3D spatially-coherent lighting. Additionally, a differentiable voxel octree cone tracing rendering layer is proposed to eliminate regular volumetric representation throughout the entire process and ensure the retention of features across different frequency domains. This reduction significantly decreases spatial usage and required floating-point operations without substantially compromising precision. Experimental results demonstrate that our approach achieves high-quality coherent estimation with minimal cost compared to previous methods.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 26 of its 43 references that are in the map. First posted on arXiv 5 Apr 2024.

Builds on

26 of 43 references.

References · 26

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2016Densely Connected Convolutional Networkscited by 6,720
  4. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
  5. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  6. 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,613
  7. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
  8. 2021PlenOctrees for Real-time Rendering of Neural Radiance Fieldscited by 905
  9. 2019Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF from a Single Imagecited by 203
  10. 2017Learning to Predict Indoor Illumination from a Single Imagecited by 161
  11. 2019Neural Inverse Rendering of an Indoor Scene from a Single Imagecited by 101
  12. 2018InverseRenderNet: Learning single image inverse renderingcited by 100
14 more
  1. 2019Deep Parametric Indoor Lighting Estimationcited by 83
  2. 2021Learning Indoor Inverse Rendering with 3D Spatially-Varying Lightingcited by 83
  3. 2022Learning-based Inverse Rendering of Complex Indoor Scenes with Differentiable Monte Carlo Raytracingcited by 78
  4. 2020OpenRooms: An End-to-End Open Framework for Photorealistic Indoor Scene Datasetscited by 74
  5. 2019Fast Spatially-Varying Indoor Lighting Estimationcited by 73
  6. 2022Dual Octree Graph Networks for Learning Adaptive Volumetric Shape Representationscited by 71
  7. 2019DeepLight: Learning Illumination for Unconstrained Mobile Mixed Realitycited by 63
  8. 2020Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illuminationcited by 63
  9. 2022Physically-Based Editing of Indoor Scene Lighting from a Single Imagecited by 62
  10. 2022IRISformer: Dense Vision Transformers for Single-Image Inverse Rendering in Indoor Scenescited by 62
  11. 2022StyleLight: HDR Panorama Generation for Lighting Estimation and Editingcited by 59
  12. 2020EMLight: Lighting Estimation via Spherical Distribution Approximationcited by 48
  13. 2019Automatic Scene Inference for 3D Object Compositingcited by 36
  14. 2020Deep Octree-based CNNs with Output-Guided Skip Connections for 3D Shape and Scene Completioncited by 33

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.