85 citations · 183 across the 7 of their papers we have counts for
10 papers
GGNN: Graph-based GPU Nearest Neighbor Search
Fabian Groh, Lukas Ruppert, Patrick Wieschollek +1
Approximate nearest neighbor (ANN) search in high dimensions is an integral part of several computer vision systems and gains importance in deep learning with explicit memory repre…
Flex-Convolution (Million-Scale Point-Cloud Learning Beyond Grid-Worlds)
Fabian Groh, Patrick Wieschollek, Hendrik P. A. Lensch
Traditional convolution layers are specifically designed to exploit the natural data representation of images -- a fixed and regular grid. However, unstructured data like 3D point…
Separating Reflection and Transmission Images in the Wild
Patrick Wieschollek, Orazio Gallo, Jinwei Gu +1
The reflections caused by common semi-reflectors, such as glass windows, can impact the performance of computer vision algorithms. State-of-the-art methods can remove reflections o…
Learning Blind Motion Deblurring
Patrick Wieschollek, Michael Hirsch, Bernhard Schölkopf +1
As handheld video cameras are now commonplace and available in every smartphone, images and videos can be recorded almost everywhere at anytime. However, taking a quick shot freque…
Efficient Large-scale Approximate Nearest Neighbor Search on the GPU
Patrick Wieschollek, Oliver Wang, Alexander Sorkine-Hornung +1
We present a new approach for efficient approximate nearest neighbor (ANN) search in high dimensional spaces, extending the idea of Product Quantization. We propose a two-level pro…
Backpropagation Training for Fisher Vectors within Neural Networks
Patrick Wieschollek, Fabian Groh, Hendrik P. A. Lensch
Fisher-Vectors (FV) encode higher-order statistics of a set of multiple local descriptors like SIFT features. They already show good performance in combination with shallow learnin…
Will People Like Your Image? Learning the Aesthetic Space
Katharina Schwarz, Patrick Wieschollek, Hendrik P. A. Lensch
Rating how aesthetically pleasing an image appears is a highly complex matter and depends on a large number of different visual factors. Previous work has tackled the aesthetic rat…
Learning Robust Video Synchronization without Annotations
Patrick Wieschollek, Ido Freeman, Hendrik P. A. Lensch
Aligning video sequences is a fundamental yet still unsolved component for a broad range of applications in computer graphics and vision. Most classical image processing methods ca…
Transfer Learning for Material Classification using Convolutional Networks
Patrick Wieschollek, Hendrik P. A. Lensch
Material classification in natural settings is a challenge due to complex interplay of geometry, reflectance properties, and illumination. Previous work on material classification…
End-to-End Learning for Image Burst Deblurring
Patrick Wieschollek, Bernhard Schölkopf, Hendrik P. A. Lensch +1
We present a neural network model approach for multi-frame blind deconvolution. The discriminative approach adopts and combines two recent techniques for image deblurring into a si…