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pro vyhledávání: '"Hadadan, Saeed"'
In this paper, we present GaNI, a Global and Near-field Illumination-aware neural inverse rendering technique that can reconstruct geometry, albedo, and roughness parameters from images of a scene captured with co-located light and camera. Existing i
Externí odkaz:
http://arxiv.org/abs/2403.15651
Inverse rendering methods that account for global illumination are becoming more popular, but current methods require evaluating and automatically differentiating millions of path integrals by tracing multiple light bounces, which remains expensive a
Externí odkaz:
http://arxiv.org/abs/2305.02192
Autor:
Hadadan, Saeed, Zwicker, Matthias
We introduce Differentiable Neural Radiosity, a novel method of representing the solution of the differential rendering equation using a neural network. Inspired by neural radiosity techniques, we minimize the norm of the residual of the differential
Externí odkaz:
http://arxiv.org/abs/2201.13190
We introduce Neural Radiosity, an algorithm to solve the rendering equation by minimizing the norm of its residual similar as in traditional radiosity techniques. Traditional basis functions used in radiosity techniques, such as piecewise polynomials
Externí odkaz:
http://arxiv.org/abs/2105.12319
Modern web services rely on Content Delivery Networks (CDNs) to efficiently deliver contents to end users. In order to minimize the experienced communication cost, it is necessary to send the end user's requests to the nearest servers. However, it is
Externí odkaz:
http://arxiv.org/abs/1902.04463