mlsanityData to 7 Oct 2026

SPLINE-Net: Sparse Photometric Stereo through Lighting Interpolation and Normal Estimation Networks

arXiv 1905.04088 ↗First posted 10 May 2019cs.CVcited by 24

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Abstract (arXiv)

This paper solves the Sparse Photometric stereo through Lighting Interpolation and Normal Estimation using a generative Network (SPLINE-Net). SPLINE-Net contains a lighting interpolation network to generate dense lighting observations given a sparse set of lights as inputs followed by a normal estimation network to estimate surface normals. Both networks are jointly constrained by the proposed symmetric and asymmetric loss functions to enforce isotropic constrain and perform outlier rejection of global illumination effects. SPLINE-Net is verified to outperform existing methods for photometric stereo of general BRDFs by using only ten images of different lights instead of using nearly one hundred images.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 May 2019.

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Citing papers per year: 2021: 5; 2022: 11; 2023: 3; 2024: 3; 2025: 1; 2026: 1.

24 papers.

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  1. 2022PS-NeRF: Neural Inverse Rendering for Multi-view Photometric Stereocited by 69
  2. 2021Shape and Material Capture at Homecited by 36
  3. 2022S3-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpointcited by 30
  4. 2022Neural Reflectance for Shape Recovery with Shadow Handlingcited by 28
  5. 2021Neural Radiance Fields Approach to Deep Multi-View Photometric Stereocited by 25
  6. 2022Self-calibrating Photometric Stereo by Neural Inverse Renderingcited by 17
  7. 2022Multi-View Photometric Stereo Revisitedcited by 17
  8. 2022PS-Transformer: Learning Sparse Photometric Stereo Network using Self-Attention Mechanismcited by 13
  9. 2022A CNN Based Approach for the Point-Light Photometric Stereo Problemcited by 10
  10. 2022Deep Learning Methods for Calibrated Photometric Stereo and Beyondcited by 10
  11. 2022DeepPS2: Revisiting Photometric Stereo Using Two Differently Illuminated Imagescited by 5
  12. 2023DANI-Net: Uncalibrated Photometric Stereo by Differentiable Shadow Handling, Anisotropic Reflectance Modeling, and Neural Inverse Renderingcited by 5
12 more
  1. 2021Leveraging Spatial and Photometric Context for Calibrated Non-Lambertian Photometric Stereocited by 4
  2. 2021Incorporating Lambertian Priors into Surface Normals Measurementcited by 4
  3. 2024Physics-Free Spectrally Multiplexed Photometric Stereo under Unknown Spectral Compositioncited by 4
  4. 2022MS-PS: A Multi-Scale Network for Photometric Stereo With a New Comprehensive Training Datasetcited by 3
  5. 2024RMAFF-PSN: A Residual Multi-Scale Attention Feature Fusion Photometric Stereo Networkcited by 1
  6. 2021Imperceptible Adversarial Examples for Fake Image Detectioncited by 0
  7. 2022NeIF: Representing General Reflectance as Neural Intrinsics Fields for Uncalibrated Photometric Stereocited by 0
  8. 2023Open-World Object Detection via Discriminative Class Prototype Learningcited by 0
  9. 2023Event Fusion Photometric Stereo Networkcited by 0
  10. 2024LIPIDS: Learning-based Illumination Planning In Discretized (Light) Space for Photometric Stereocited by 0
  11. 2025PS-EIP: Robust Photometric Stereo Based on Event Interval Profilecited by 0
  12. 2026FlashNormal: Detailed Surface Normal Estimation from Flash and No-Flash Imagescited by 0