3DNet: Large-scale object class recognition from CAD models

Wohlkinger, Walter; Aldóma, Aitor; Rusu, Radu Bogdan; Vincze, Markus

doi:10.1109/icra.2012.6225116

Cited by 130 publications

(93 citation statements)

References 19 publications

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“…For more comprehensive comparisons, besides these 20 14 , from top left to bottom right is the chronological order from the video. The curves under the images are the accelerometer data at 50 Hz of devices attached to the knife, the mixing spoon, the small spoon, the peeler, the glass, the oil bottle, and the pepper dispenser mentioned datasets above, another 26 extra RGB-D datasets for different applications are also added into the tables: Birmingham University Objects, Category Modeling RGB-D [104], Cornell Activity [47,92], Cornell RGB-D [48], DGait [12], Daily Activities with occlusions [1], Heidelberg University Scenes [63], Microsoft 7-scenes [78], MobileRGBD [96], MPII Multi-Kinect [93], MSR Action3D Dataset [97], MSR 3D Online Action [103], MSRGesture3D [50], DAFT [31], Paper Kinect [70], RGBD-HuDaAct [68], Stanford Scene Object [44], Stanford 3D Scene [105], Sun3D [101], SUN RGB-D [82], TST Fall Detection [28], UTD-MHAD [14], Vienna University Technology Object [2], Willow Garage [99], Workout SU-10 exercise [67] and 3D-Mask [24]. In addition, we name those datasets without original names by means of creation place or applications.…”

Section: Discussionmentioning

confidence: 99%

RGB-D datasets using microsoft kinect or similar sensors: a survey

Cai

Han

Liu

et al. 2016

Multimed Tools Appl

128

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RGB-D data has turned out to be a very useful representation of an indoor scene for solving fundamental computer vision problems. It takes the advantages of the color image that provides appearance information of an object and also the depth image that is immune to the variations in color, illumination, rotation angle and scale. With the invention of the low-cost Microsoft Kinect sensor, which was initially used for gaming and later became a popular device for computer vision, high quality RGB-D data can be acquired easily. In recent years, more and more RGB-D image/video datasets dedicated to various applications have become available, which are of great importance to benchmark the stateof-the-art. In this paper, we systematically survey popular RGB-D datasets for different applications including object recognition, scene classification, hand gesture recognition, 3D-simultaneous localization and mapping, and pose estimation. We provide the insights into the characteristics of each important dataset, and compare the popularity and the difficulty of those datasets. Overall, the main goal of this survey is to give a comprehensive description about the available RGB-D datasets and thus to guide researchers in the selection of suitable datasets for evaluating their algorithms.

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Section: Discussionmentioning

confidence: 99%

RGB-D datasets using microsoft kinect or similar sensors: a survey

Cai

Han

Liu

et al. 2016

Multimed Tools Appl

128

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“…5 gezeigten Objekte nie vorher gesehen wurden, es wurde ja nur von Modellen aus dem Internet gelernt. Ebenso ist das Lernen neuer Klassen rasch möglich, ohne dem System viele Beispielobjekte zeigen zu müssen [29].…”

Section: Abb 6 Objekterkennung Mit Starken Verdeckungen: Mit Hilfe unclassified

Roboter lernen mit Gegenständen umzugehen: neue Entwicklungen und Chancen

Vincze

Zillich

Prankl

2017

Elektrotech. Inftech.

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Für den zukünftigen Einsatz von Robotern fordern Experten aus der Industrie Roboter, die sicher sind und dem Benutzer eine klare Rückmeldung geben, was der Roboter macht und warum. Eine der wichtigsten Funktionen, um dies zu erreichen, ist die zuverlässige Erkennung der Umgebung, um den Roboter mit einem besseren Verstehen der Welt auszustatten. In diesem Artikel geben wir eine Übersicht derzeitiger Entwicklungen.

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“…Similar to the simulated experiments, the task of the robot was to find a cup in an area of our robot lab (40m 2 ). For recognizing objects in the environment we used the an object recognition framework based on 3D CAD models [12] which is integrated in PCL 5 . For recognizing cups (mugs) we trained a classifier based on 50 object categories.…”

Section: B Real World Experimentsmentioning

confidence: 99%

Using Qualitative Spatial Relations for indirect object search

Kunze

Doreswamy

Hawes

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Finding objects in human environments requires autonomous mobile robots to reason about potential object locations and to plan to perceive them accordingly. By using information about the 3D structure of the environment, knowledge about landmark objects and their spatial relationship to the sought object, search can be improved by directing the robot towards the most likely object locations.In this paper we have designed, implemented and evaluated an approach for searching for objects on the basis of Qualitative Spatial Relations (QSRs) such as left-of and in-front-of. On the basis of QSRs between landmarks and the sought object we generate metric poses of potential object locations using an extended version of the ternary point calculus and employ this information for view planning. Preliminary results show that search methods based on QSRs are faster and more reliable than methods not considering them.

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3DNet: Large-scale object class recognition from CAD models

Cited by 130 publications

References 19 publications

RGB-D datasets using microsoft kinect or similar sensors: a survey

RGB-D datasets using microsoft kinect or similar sensors: a survey

Roboter lernen mit Gegenständen umzugehen: neue Entwicklungen und Chancen

Using Qualitative Spatial Relations for indirect object search

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