Dense Multi-view 3D-reconstruction Without Dense Correspondences
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Abstract (arXiv)
We introduce a variational method for multi-view shape-from-shading under natural illumination. The key idea is to couple PDE-based solutions for single-image based shape-from-shading problems across multiple images and multiple color channels by means of a variational formulation. Rather than alternatingly solving the individual SFS problems and optimizing the consistency across images and channels which is known to lead to suboptimal results, we propose an efficient solution of the coupled problem by means of an ADMM algorithm. In numerous experiments on both simulated and real imagery, we demonstrate that the proposed fusion of multiple-view reconstruction and shape-from-shading provides highly accurate dense reconstructions without the need to compute dense correspondences. With the proposed variational integration across multiple views shape-from-shading techniques become applicable to challenging real-world reconstruction problems, giving rise to highly detailed geometry even in areas of smooth brightness variation and lacking texture.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Apr 2017.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 2.
5 papers.
- 2021Unified Shape and SVBRDF Recovery using Differentiable Monte Carlo Renderingcited by 71
- 2023Diffusion 3D Features (Diff3F): Decorating Untextured Shapes with Distilled Semantic Featurescited by 55
- 2021Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibrationcited by 48
- 2023Towards Scalable Multi-View Reconstruction of Geometry and Materialscited by 3
- 2021Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fieldscited by 1
- 2023Diffusion 3D Features (Diff3F): Decorating Untextured Shapes with Distilled Semantic Features28 Nov 2023
- 2023Towards Scalable Multi-View Reconstruction of Geometry and Materials6 Jun 2023
- 2021Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields13 Jul 2021
- 2021Unified Shape and SVBRDF Recovery using Differentiable Monte Carlo Rendering28 Mar 2021
- 2021Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration24 Feb 2021