Real Shading in Unreal Engine 4 by

By Brian Karis. 2013

335 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/91ee695f6a64d8508817fa3c0203d4389c462536

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Citations in the corpus

GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images (2022, methodology): “When equipped with a physics-based rendering models, GET3D successfully predicts reasonable surface material parameters, generating delicate models which can be directly utilized in stand rendering engines like Blender [12] and Unreal [17].”
Extracting Triangular 3D Models, Materials, and Lighting From Images (2021, methodology): “Following Karis [28], this term is also pre-integrated and represented by a filtered cubemap.”
Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF From a Single Image (2019, background): “Our materials, on the other hand, are represented by a physically motivated microfacet BRDF model [25]1.”
UniSim: A Neural Closed-Loop Sensor Simulator (2023, unknown): “Most prior sensor simulation systems use 3D-scanned or manually built synthetic environments for small indoor environments [33, 37, 67], and perform rasterization or ray-tracing [58,69] to simulate various sensor data [20,22,30].”
Synthetic Data for Deep Learning (2019, background): “CARLA (CAR Learning to Act) [153] is an open simulator for urban driving, developed as an open-source layer over Unreal Engine 4 [310].”
OpenRooms: An End-to-End Open Framework for Photorealistic Indoor Scene Datasets (2020, methodology): “We use the simplified microfacet BRDF model of [23].”
OpenRooms: An Open Framework for Photorealistic Indoor Scene Datasets (2020, methodology): “Since OpenRooms provides complete control over mapping arbitrary semantically-meaningful materials to indoor surfaces, such ground truth may enable future studies in inferring physical properties from images, or learning robotics tasks conditioned on material properties.”
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles (2017, methodology): “Since advanced rendering and detailed environments have been a key requirement for AirSim we chose Unreal Engine 4 (UE4) [11] as our rendering platform.”