153 citations · 216 across the 4 of their papers we have counts for
5 papers · 1 filter
Curiosity-driven 3D Object Detection Without Labels
David Griffiths, Jan Boehm, Tobias Ritschel
In this paper we set out to solve the task of 6-DOF 3D object detection from 2D images, where the only supervision is a geometric representation of the objects we aim to find. In d…
Finding Your (3D) Center: 3D Object Detection Using a Learned Loss
David Griffiths, Jan Boehm, Tobias Ritschel
Massive semantically labeled datasets are readily available for 2D images, however, are much harder to achieve for 3D scenes. Objects in 3D repositories like ShapeNet are labeled,…
SynthCity: A large scale synthetic point cloud
David Griffiths, Jan Boehm
With deep learning becoming a more prominent approach for automatic classification of three-dimensional point cloud data, a key bottleneck is the amount of high quality training da…
A review on deep learning techniques for 3D sensed data classification
David Griffiths, Jan Boehm
Over the past decade deep learning has driven progress in 2D image understanding. Despite these advancements, techniques for automatic 3D sensed data understanding, such as point c…
Weighted Point Cloud Augmentation for Neural Network Training Data Class-Imbalance
David Griffiths, Jan Boehm
Recent developments in the field of deep learning for 3D data have demonstrated promising potential for end-to-end learning directly from point clouds. However, many real-world poi…