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Publication type: Article in Proceedings
Author: Jesse Richter-Klug, Constantin Wellhausen, Udo Frese
Title: ESKO6d-A Binocular and RGB-D Dataset of Stored Kitchen Objects with 6d Poses
Book / Collection title: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Page(s): 893 – 899
Year published: 2019
Abstract: We present a new dataset with the goal of advancing the state-of-the-art in object pose estimation especially for stored porcelain and glass crockery in kitchen scenes. Specifically the ESKO 6d (EASE Stored Kitchen Objects with 6d poses) dataset features texture-less, glossy or glassy ordinary used objects which were naturally stored in a cupboard, drawer or dishwasher. There is a large degree of occlusion being the specific challenge in these scenes. Each scene was recorded in video sequences by two cameras (RGB-D (Kinect) and binocular) within multiple setup stages. The dataset contains an RGB-D image or binocular RGB image plus stereo-matched depth image as well as 6d pose ground truth and instance segmentation. Our dataset contains twelve stored object scenes with a combined amount of 47 video sequences captured by each camera, resulting in over 17k annotated Kinect images and more than 42k annotated stereo images showing around 50 different objects. The ground truth annotation is precise to 3. 5mm ADD (details see paper). The dataset can be accessed under http://www.informatik.uni-bremen.de/esko6d-dataset. Besides the concrete dataset we propose a method of ground truth pose measurement based on an external 3d tracking system that allows on the one hand to precisely measure the object’s pose inside a tight packed storage and on the other hand to obtain the object pose in several images with just one manual measurement.
Internet: https://ieeexplore.ieee.org/document/8967937
PDF Version: http://www.informatik.uni-bremen.de/agebv2/downloads/published/richterklugiros19_final.pdf
Status: Reviewed
Last updated: 07. 02. 2022

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