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20122023
most citedMonte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds

204 citations · 609 across the 30 of their papers we have counts for

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Showing 2018Show all

5 papers · 1 filter

cs.GR2018★ 65 cited

Inviwo -- A Visualization System with Usage Abstraction Levels

Daniel Jönsson, Peter Steneteg, Erik Sundén +6

The complexity of today's visualization applications demands specific visualization systems tailored for the development of these applications. Frequently, such systems utilize lev…

cs.GR2018

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…

cs.CV2018★ 25 cited

Classifier-Guided Visual Correction of Noisy Labels for Image Classification Tasks

Alex Bäuerle, Heiko Neumann, Timo Ropinski

Training data plays an essential role in modern applications of machine learning. However, gathering labeled training data is time-consuming. Therefore, labeling is often outsource…

cs.GR2018

Void Space Surfaces to Convey Depth in Vessel Visualizations

Julian Kreiser, Pedro Hermosilla, Timo Ropinski

To enhance depth perception and thus data comprehension, additional depth cues are often used in 3D visualizations of complex vascular structures. Accordingly, there is a variety o…

cs.CV2018★ 204 cited

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…