204 citations · 703 across the 41 of their papers we have counts for
8 papers · 1 filter
Learning to Predict Image-based Rendering Artifacts with Respect to a Hidden Reference Image
Mojtaba Bemana, Joachim Keinert, Karol Myszkowski +4
Image metrics predict the perceived per-pixel difference between a reference image and its degraded (e. g., re-rendered) version. In several important applications, the reference i…
Escaping Plato's Cave: 3D Shape From Adversarial Rendering
Philipp Henzler, Niloy Mitra, Tobias Ritschel
We introduce PlatonicGAN to discover the 3D structure of an object class from an unstructured collection of 2D images, i.e., where no relation between photos is known, except that…
Deep-learning the Latent Space of Light Transport
Pedro Hermosilla, Sebastian Maisch, Tobias Ritschel +1
We suggest a method to directly deep-learn light transport, i. e., the mapping from a 3D geometry-illumination-material configuration to a shaded 2D image. While many previous lear…
End-to-end Sampling Patterns
Thomas Leimkühler, Gurprit Singh, Karol Myszkowski +2
Sample patterns have many uses in Computer Graphics, ranging from procedural object placement over Monte Carlo image synthesis to non-photorealistic depiction. Their properties suc…
Perceptual Rasterization for Head-mounted Display Image Synthesis
Tobias Ritschel, Sebastian Friston, Anthony Steed
We suggest a rasterization pipeline tailored towards the need of head-mounted displays (HMD), where latency and field-of-view requirements pose new challenges beyond those of tradi…
Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds
Pedro Hermosilla, Tobias Ritschel, Pere-Pau Vázquez +2
Deep learning systems extensively use convolution operations to process input data. Though convolution is clearly defined for structured data such as 2D images or 3D volumes, this…
Learning on the Edge: Explicit Boundary Handling in CNNs
Carlo Innamorati, Tobias Ritschel, Tim Weyrich +1
Convolutional neural networks (CNNs) handle the case where filters extend beyond the image boundary using several heuristics, such as zero, repeat or mean padding. These schemes ar…
Deep Appearance Maps
Maxim Maximov, Laura Leal-Taixé, Mario Fritz +1
We propose a deep representation of appearance, i. e., the relation of color, surface orientation, viewer position, material and illumination. Previous approaches have useddeep lea…