12 citations · 28 across the 8 of their papers we have counts for
6 papers · 1 filter
ISAR: A Benchmark for Single- and Few-Shot Object Instance Segmentation and Re-Identification
Nicolas Gorlo, Kenneth Blomqvist, Francesco Milano +1
Most object-level mapping systems in use today make use of an upstream learned object instance segmentation model. If we want to teach them about a new object or segmentation class…
Panoptic Vision-Language Feature Fields
Haoran Chen, Kenneth Blomqvist, Francesco Milano +1
Recently, methods have been proposed for 3D open-vocabulary semantic segmentation. Such methods are able to segment scenes into arbitrary classes based on text descriptions provide…
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…
3D Annotation Of Arbitrary Objects In The Wild
Kenneth Blomqvist, Julius Hietala
Recent years have produced a variety of learning based methods in the context of computer vision and robotics. Most of the recently proposed methods are based on deep learning, whi…
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…