# Deep Reflectance Maps

arXiv: https://arxiv.org/abs/1511.04384 · HTML: https://mlsanity.com/paper/1511.04384/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2015.

## Built on by (most cited first, 13 of 13)

- [DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision](https://mlsanity.com/paper/2312.16256/) (2023)
- [A Survey on Intrinsic Images: Delving Deep Into Lambert and Beyond](https://mlsanity.com/paper/2112.03842/) (2021)
- [Shape and Material Capture at Home](https://mlsanity.com/paper/2104.06397/) (2021)
- [Generative Modelling of BRDF Textures from Flash Images](https://mlsanity.com/paper/2102.11861/) (2021)
- [NeuMIP: Multi-Resolution Neural Materials](https://mlsanity.com/paper/2104.02789/) (2021)
- [Diffusion Reflectance Map: Single-Image Stochastic Inverse Rendering of Illumination and Reflectance](https://mlsanity.com/paper/2312.04529/) (2023)
- [Learning to Learn and Sample BRDFs](https://mlsanity.com/paper/2210.03510/) (2022)
- [MERLiN: Single-Shot Material Estimation and Relighting for Photometric Stereo](https://mlsanity.com/paper/2409.00674/) (2024)
- [Local-to-Global Panorama Inpainting for Locale-Aware Indoor Lighting Prediction](https://mlsanity.com/paper/2303.10344/) (2023)
- [Tree-Structured Shading Decomposition](https://mlsanity.com/paper/2309.07122/) (2023)
- [Predicting Surface Reflectance Properties of Outdoor Scenes Under Unknown Natural Illumination](https://mlsanity.com/paper/2105.06820/) (2021)
- [Correspondences of the Third Kind: Camera Pose Estimation from Object Reflection](https://mlsanity.com/paper/2312.04527/) (2023)
- [Under One Sun: Multi-Object Generative Perception of Materials and Illumination](https://mlsanity.com/paper/2603.19226/) (2026)

## Abstract (arXiv)

Undoing the image formation process and therefore decomposing appearance into its intrinsic properties is a challenging task due to the under-constraint nature of this inverse problem. While significant progress has been made on inferring shape, materials and illumination from images only, progress in an unconstrained setting is still limited. We propose a convolutional neural architecture to estimate reflectance maps of specular materials in natural lighting conditions. We achieve this in an end-to-end learning formulation that directly predicts a reflectance map from the image itself. We show how to improve estimates by facilitating additional supervision in an indirect scheme that first predicts surface orientation and afterwards predicts the reflectance map by a learning-based sparse data interpolation. In order to analyze performance on this difficult task, we propose a new challenge of Specular MAterials on SHapes with complex IllumiNation (SMASHINg) using both synthetic and real images. Furthermore, we show the application of our method to a range of image-based editing tasks on real images.