# LIPIDS: Learning-based Illumination Planning In Discretized (Light) Space for Photometric Stereo

arXiv: https://arxiv.org/abs/2409.02716 · HTML: https://mlsanity.com/paper/2409.02716/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 36 references that are in the map.
- First posted on arXiv 1 Sep 2024.

## Builds on (most cited first, 12 of 12)

- [PS-FCN: A Flexible Learning Framework for Photometric Stereo](https://mlsanity.com/paper/1807.08696/) (2018)
- [Self-calibrating Deep Photometric Stereo Networks](https://mlsanity.com/paper/1903.07366/) (2019)
- [CNN-PS: CNN-based Photometric Stereo for General Non-Convex Surfaces](https://mlsanity.com/paper/1808.10093/) (2018)
- [Shape and Material Capture at Home](https://mlsanity.com/paper/2104.06397/) (2021)
- [Scalable, Detailed and Mask-Free Universal Photometric Stereo](https://mlsanity.com/paper/2303.15724/) (2023)
- [Learning Sensor Multiplexing Design through Back-propagation](https://mlsanity.com/paper/1605.07078/) (2016)
- [SPLINE-Net: Sparse Photometric Stereo through Lighting Interpolation and Normal Estimation Networks](https://mlsanity.com/paper/1905.04088/) (2019)
- [PX-NET: Simple and Efficient Pixel-Wise Training of Photometric Stereo Networks](https://mlsanity.com/paper/2008.04933/) (2020)
- [PS-Transformer: Learning Sparse Photometric Stereo Network using Self-Attention Mechanism](https://mlsanity.com/paper/2211.11386/) (2022)
- [Deep Learning Methods for Calibrated Photometric Stereo and Beyond](https://mlsanity.com/paper/2212.08414/) (2022)
- [DeepPS2: Revisiting Photometric Stereo Using Two Differently Illuminated Images](https://mlsanity.com/paper/2207.02025/) (2022)
- [An Optimal Experimental Design Approach for Light Configurations in Photometric Stereo](https://mlsanity.com/paper/2204.05218/) (2022)

## Abstract (arXiv)

Photometric stereo is a powerful method for obtaining per-pixel surface normals from differently illuminated images of an object. While several methods address photometric stereo with different image (or light) counts ranging from one to two to a hundred, very few focus on learning optimal lighting configuration. Finding an optimal configuration is challenging due to the vast number of possible lighting directions. Moreover, exhaustively sampling all possibilities is impractical due to time and resource constraints. Photometric stereo methods have demonstrated promising performance on existing datasets, which feature limited light directions sparsely sampled from the light space. Therefore, can we optimally utilize these datasets for illumination planning? In this work, we introduce LIPIDS - Learning-based Illumination Planning In Discretized light Space to achieve minimal and optimal lighting configurations for photometric stereo under arbitrary light distribution. We propose a Light Sampling Network (LSNet) that optimizes lighting direction for a fixed number of lights by minimizing the normal loss through a normal regression network. The learned light configurations can directly estimate surface normals during inference, even using an off-the-shelf photometric stereo method. Extensive qualitative and quantitative analyses on synthetic and real-world datasets show that photometric stereo under learned lighting configurations through LIPIDS either surpasses or is nearly comparable to existing illumination planning methods across different photometric stereo backbones.