# Surface Light Field Fusion

arXiv: https://arxiv.org/abs/1809.02057 · HTML: https://mlsanity.com/paper/1809.02057/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2018.

## Built on by (most cited first, 4 of 4)

- [Unified Shape and SVBRDF Recovery using Differentiable Monte Carlo Rendering](https://mlsanity.com/paper/2103.15208/) (2021)
- [Towards Scalable Multi-View Reconstruction of Geometry and Materials](https://mlsanity.com/paper/2306.03747/) (2023)
- [Efficient Multi-View Inverse Rendering Using a Hybrid Differentiable Rendering Method](https://mlsanity.com/paper/2308.10003/) (2023)
- [A Multiple-View Geometric Model for Specularity Prediction on General Curved Surfaces](https://mlsanity.com/paper/2108.09378/) (2021)

## 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.