# Flexible SVBRDF Capture with a Multi-Image Deep Network

## TL;DR

This paper introduces a deep learning approach for estimating spatially-varying material reflectance from a variable number of uncalibrated images, enabling high-quality SVBRDF capture with minimal input images.

## Contribution

The proposed method uses an order-independent fusion layer to effectively combine multiple images, improving material estimation without calibration or extensive data.

## Key findings

- Achieves high-quality SVBRDF reconstruction with 1-10 images.
- Outperforms single-image methods in accuracy.
- Handles view and light variation without calibration.

## Abstract

Empowered by deep learning, recent methods for material capture can estimate a spatially-varying reflectance from a single photograph. Such lightweight capture is in stark contrast with the tens or hundreds of pictures required by traditional optimization-based approaches. However, a single image is often simply not enough to observe the rich appearance of real-world materials. We present a deep-learning method capable of estimating material appearance from a variable number of uncalibrated and unordered pictures captured with a handheld camera and flash. Thanks to an order-independent fusing layer, this architecture extracts the most useful information from each picture, while benefiting from strong priors learned from data. The method can handle both view and light direction variation without calibration. We show how our method improves its prediction with the number of input pictures, and reaches high quality reconstructions with as little as 1 to 10 images -- a sweet spot between existing single-image and complex multi-image approaches.