The Poliigon site acts as an asset resource for 3D artists, and everything that you'll find on it comes directly from Andrew and his team.

If you're a fan, you've likely heard him mention his own pet brand, Poliigon. We also provide an overview of current publicly available datasets for neural lighting applications.Only a very special type of curmudgeon can claim to hate Andrew Price, the energetic personality behind the hugely-popular Blender Guru YouTube channel. We examine in detail the attributes of the proposed approaches, presented in three categories: scene illumination estimation, relighting with reflectance‐aware scene‐specific representations and finally relighting as image‐to‐image transformations. This contribution reviews the current advances in the application of deep neural computing to illumination estimation and relighting. We also provide an overview of current publicly available datasets for neural lighting applications. Each category is concluded with a discussion on the main characteristics of the current methods and possible future trends. This contribution aims to bring together in a coherent manner current advances in this conjunction.

Recently, the application of deep neural computing to illumination estimation, relighting and inverse rendering has shown promising results. Classical inverse rendering approaches aim to decompose a scene into its orthogonal constituting elements, namely scene geometry, illumination and surface materials, which can later be used for augmented reality or to render new images under novel lighting or viewpoints. Scene relighting and estimating illumination of a real scene for insertion of virtual objects in a mixed‐reality scenario are well‐studied challenges in the computer vision and graphics fields.

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