# Neural Acquisition & Representation of Subsurface Scattering

arXiv: https://arxiv.org/abs/2606.02292 · HTML: https://mlsanity.com/paper/2606.02292/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 42 references that are in the map.
- First posted on arXiv 1 Jun 2026.

## Builds on (most cited first, 3 of 3)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Learning Object-Centric Neural Scattering Functions for Free-Viewpoint Relighting and Scene Composition](https://mlsanity.com/paper/2303.06138/) (2023)
- [Fast Image-based Neural Relighting with Translucency-Reflection Modeling](https://mlsanity.com/paper/2306.09322/) (2023)

## Built on by (most cited first, 1 of 1)

- [From Phase to Phenomenon: Self-Supervised Learning of Subsurface Scattering with Minimal Phase-shift Inputs](https://mlsanity.com/paper/2606.29461/) (2026)

## Abstract (arXiv)

We present a method to acquire and estimate the sub-surface scattering properties of light transport at a highly detailed level by learning the pixel footprint response at each point on the object surface. The reconstruction leverages 3D scanning techniques as input to a U-Net CNN. A stereo projector-camera setup using phase-shifted profilometry (PSP) patterns efficiently captures the data for a variety of scattering objects. Reconstructing dense pixel footprints allows for relighting with arbitrary high-resolution projector patterns. The final output is a relit color image. Qualitative and quantitative comparison against illuminated real-world captured images demonstrate that the predicted footprints are almost identical to the actual responses. The same model is trained for multiple views across multiple objects such that the learned representations can be used to generalize to unseen sub-surface scattering materials as well.