Surface Light Field Fusion
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Abstract (arXiv)
We present an approach for interactively scanning highly reflective objects with a commodity RGBD sensor. In addition to shape, our approach models the surface light field, encoding scene appearance from all directions. By factoring the surface light field into view-independent and wavelength-independent components, we arrive at a representation that can be robustly estimated with IR-equipped commodity depth sensors, and achieves high quality results.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Sep 2018.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 2.
4 papers.
- 2021Unified Shape and SVBRDF Recovery using Differentiable Monte Carlo Renderingcited by 71
- 2023Towards Scalable Multi-View Reconstruction of Geometry and Materialscited by 3
- 2023Efficient Multi-View Inverse Rendering Using a Hybrid Differentiable Rendering Methodcited by 1
- 2021A Multiple-View Geometric Model for Specularity Prediction on General Curved Surfacescited by 0
- 2023Efficient Multi-View Inverse Rendering Using a Hybrid Differentiable Rendering Method19 Aug 2023
- 2023Towards Scalable Multi-View Reconstruction of Geometry and Materials6 Jun 2023
- 2021A Multiple-View Geometric Model for Specularity Prediction on General Curved Surfaces20 Aug 2021
- 2021Unified Shape and SVBRDF Recovery using Differentiable Monte Carlo Rendering28 Mar 2021