Variational Uncalibrated Photometric Stereo under General Lighting
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Abstract (arXiv)
Photometric stereo (PS) techniques nowadays remain constrained to an ideal laboratory setup where modeling and calibration of lighting is amenable. To eliminate such restrictions, we propose an efficient principled variational approach to uncalibrated PS under general illumination. To this end, the Lambertian reflectance model is approximated through a spherical harmonic expansion, which preserves the spatial invariance of the lighting. The joint recovery of shape, reflectance and illumination is then formulated as a single variational problem. There the shape estimation is carried out directly in terms of the underlying perspective depth map, thus implicitly ensuring integrability and bypassing the need for a subsequent normal integration. To tackle the resulting nonconvex problem numerically, we undertake a two-phase procedure to initialize a balloon-like perspective depth map, followed by a "lagged" block coordinate descent scheme. The experiments validate efficiency and robustness of this approach. Across a variety of evaluations, we are able to reduce the mean angular error consistently by a factor of 2-3 compared to the state-of-the-art.
Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Apr 2019.
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Citing papers per year: 2021: 2; 2022: 6; 2023: 1; 2024: 2; 2025: 3.
14 papers.
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- 2021Shape and Material Capture at Homecited by 36
- 2023Scalable, Detailed and Mask-Free Universal Photometric Stereocited by 30
- 2022Universal Photometric Stereo Network using Global Lighting Contextscited by 18
- 2022Self-calibrating Photometric Stereo by Neural Inverse Renderingcited by 17
- 2022Fast Light-Weight Near-Field Photometric Stereocited by 12
- 2025Light of Normals: Unified Feature Representation for Universal Photometric Stereocited by 9
- 2024Sparse Views, Near Light: A Practical Paradigm for Uncalibrated Point-light Photometric Stereocited by 6
- 2022High-Quality RGB-D Reconstruction via Multi-View Uncalibrated Photometric Stereo and Gradient-SDFcited by 5
- 2025Multi-view Surface Reconstruction Using Normal and Reflectance Cuescited by 5
- 2022MS-PS: A Multi-Scale Network for Photometric Stereo With a New Comprehensive Training Datasetcited by 3
- 2021Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fieldscited by 1
- 2024Spin-UP: Spin Light for Natural Light Uncalibrated Photometric Stereocited by 1
2 more
- 2022Polarimetric Multi-View Inverse Renderingcited by 0
- 2025MoAngelo: Motion-Aware Neural Surface Reconstruction for Dynamic Scenescited by 0
- 2025MoAngelo: Motion-Aware Neural Surface Reconstruction for Dynamic Scenes19 Sep 2025
- 2025Light of Normals: Unified Feature Representation for Universal Photometric Stereo23 Jun 2025
- 2025Multi-view Surface Reconstruction Using Normal and Reflectance Cues4 Jun 2025
- 2024Spin-UP: Spin Light for Natural Light Uncalibrated Photometric Stereo2 Apr 2024
- 2024Sparse Views, Near Light: A Practical Paradigm for Uncalibrated Point-light Photometric Stereo29 Mar 2024
- 2023Scalable, Detailed and Mask-Free Universal Photometric Stereo28 Mar 2023
- 2022Polarimetric Multi-View Inverse Rendering24 Dec 2022
- 2022MS-PS: A Multi-Scale Network for Photometric Stereo With a New Comprehensive Training Dataset25 Nov 2022
- 2022High-Quality RGB-D Reconstruction via Multi-View Uncalibrated Photometric Stereo and Gradient-SDF21 Oct 2022
- 2022Self-calibrating Photometric Stereo by Neural Inverse Rendering16 Jul 2022
- 2022Universal Photometric Stereo Network using Global Lighting Contexts6 Jun 2022
- 2022Fast Light-Weight Near-Field Photometric Stereo30 Mar 2022
- 2021Shape and Material Capture at Homecited by 36
- 2023Scalable, Detailed and Mask-Free Universal Photometric Stereocited by 30
- 2022Universal Photometric Stereo Network using Global Lighting Contextscited by 18
- 2022Self-calibrating Photometric Stereo by Neural Inverse Renderingcited by 17
- 2022Fast Light-Weight Near-Field Photometric Stereocited by 12
- 2025Light of Normals: Unified Feature Representation for Universal Photometric Stereocited by 9
- 2024Sparse Views, Near Light: A Practical Paradigm for Uncalibrated Point-light Photometric Stereocited by 6
- 2022High-Quality RGB-D Reconstruction via Multi-View Uncalibrated Photometric Stereo and Gradient-SDFcited by 5
- 2025Multi-view Surface Reconstruction Using Normal and Reflectance Cuescited by 5
- 2022MS-PS: A Multi-Scale Network for Photometric Stereo With a New Comprehensive Training Datasetcited by 3
- 2021Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fieldscited by 1
- 2024Spin-UP: Spin Light for Natural Light Uncalibrated Photometric Stereocited by 1
12 most cited of 14 All 14 (JSON)
- 2025MoAngelo: Motion-Aware Neural Surface Reconstruction for Dynamic Scenes19 Sep 2025
- 2025Light of Normals: Unified Feature Representation for Universal Photometric Stereo23 Jun 2025
- 2025Multi-view Surface Reconstruction Using Normal and Reflectance Cues4 Jun 2025
- 2024Spin-UP: Spin Light for Natural Light Uncalibrated Photometric Stereo2 Apr 2024
- 2024Sparse Views, Near Light: A Practical Paradigm for Uncalibrated Point-light Photometric Stereo29 Mar 2024
- 2023Scalable, Detailed and Mask-Free Universal Photometric Stereo28 Mar 2023
- 2022Polarimetric Multi-View Inverse Rendering24 Dec 2022
- 2022MS-PS: A Multi-Scale Network for Photometric Stereo With a New Comprehensive Training Dataset25 Nov 2022
- 2022High-Quality RGB-D Reconstruction via Multi-View Uncalibrated Photometric Stereo and Gradient-SDF21 Oct 2022
- 2022Self-calibrating Photometric Stereo by Neural Inverse Rendering16 Jul 2022
- 2022Universal Photometric Stereo Network using Global Lighting Contexts6 Jun 2022
- 2022Fast Light-Weight Near-Field Photometric Stereo30 Mar 2022
12 newest of 14 All 14 (JSON)