5 citations · 6 across the 3 of their papers we have counts for
5 papers
Baking in the Feature: Accelerating Volumetric Segmentation by Rendering Feature Maps
Kenneth Blomqvist, Lionel Ott, Jen Jen Chung +1
Methods have recently been proposed that densely segment 3D volumes into classes using only color images and expert supervision in the form of sparse semantically annotated pixels.…
Semi-automatic 3D Object Keypoint Annotation and Detection for the Masses
Kenneth Blomqvist, Jen Jen Chung, Lionel Ott +1
Creating computer vision datasets requires careful planning and lots of time and effort. In robotics research, we often have to use standardized objects, such as the YCB object set…
Points2Vec: Unsupervised Object-level Feature Learning from Point Clouds
Joël Bachmann, Kenneth Blomqvist, Julian Förster +1
Unsupervised representation learning techniques, such as learning word embeddings, have had a significant impact on the field of natural language processing. Similar representation…
Go Fetch: Mobile Manipulation in Unstructured Environments
Kenneth Blomqvist, Michel Breyer, Andrei Cramariuc +7
With humankind facing new and increasingly large-scale challenges in the medical and domestic spheres, automation of the service sector carries a tremendous potential for improved…
Deep convolutional Gaussian processes
Kenneth Blomqvist, Samuel Kaski, Markus Heinonen
We propose deep convolutional Gaussian processes, a deep Gaussian process architecture with convolutional structure. The model is a principled Bayesian framework for detecting hier…