The KIT object models database: An object model database for object recognition, localization and manipulation in service robotics

By Alexander Kasper, Zhixing Xue, Rüdiger Dillmann. 2012

275 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Geology, Robotics & Control

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Citations in the corpus

IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data (2019, methodology): “om teleoperation data. Large-Scale Data Collection in Robotics: Self-Supervised Learning has been employed to collect and learn from large amounts of data for tasks such as grasping in both simulated [12,17,24] and physical settings [16,21,28]. These methods collected hundreds of hours of robot inte”
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning (2019, background): “Similarly, there exists a number of robotics simulation benchmarks including visual navigation [39, 40, 41, 42, 43], autonomous driving [44, 45, 46], grasping [47, 48, 49], single-task manipulation [50], among others.”
ROBOTURK: A Crowdsourcing Platform for Robotic Skill Learning through Imitation (2018, methodology): “SSL has been used successfully to collect and learn from large quantities of data in both simulated [7, 8, 9] and physical settings [5, 10, 11] for tasks such as grasping.”
Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics (2017, neutral): “0: 1,371 synthetic models from 3DNet [63] and 129 laser scans from the KIT object database [26].”
Dex-Net 3.0: Computing Robust Robot Suction Grasp Targets in Point Clouds using a New Analytic Model and Deep Learning (2017, methodology): “A GQ-CNN trained on synthetic data from 3DNet [35], KIT [16], and the Adversarial objects.”
Yale-CMU-Berkeley dataset for robotic manipulation research (2017, background): “Compared to other object datasets in the literature (including ones widely utilized by the robotics community (Goldfeder et al., 2009; Kasper et al., 2012; Singh et al., 2014; a comprehensive overview is given by Calli et al., 2015b), our dataset has four major advantages.”
Objaverse: A Universe of Annotated 3D Objects (2022, background): “Datasets such as KIT [33], YCB [5], BigBIRD [64], IKEA [40], and Pix3D [66] provide image-calibrated models over a diverse set of household objects, but severely lack in scale with only a few hundred objects at most.”
BigBIRD: A large-scale 3D database of object instances (2014, result): “The closest work to ours is that of Kasper et al. [20].”
The YCB object and Model set: Towards common benchmarks for manipulation research (2015, methodology): “Even though there are a large number of efforts (e.g. [1, 2, 4, 12-15]) that provide mesh model datasets of objects, these datasets have limited utility for manipulation benchmarking due to several reasons: First, since most of them are not designed specifically for manipulation benchmarking, the…”
Image Object Label 3 D CAD Model Candidate Grasps Google Object Recognition Engine Google Cloud Storage Select Feasible Grasp with Highest Success Probability Pose EstimationCamera Robots Cloud 3 D Sensor (2014, methodology): “The Columbia Grasp dataset [67], the MIT KIT object dataset [88], and the Willow Garage Household Objects Database [40] are available online and have been used to evaluate different aspects of grasping algorithms, including grasp stability [45], [46], robust grasping [162], and scene understanding…”
Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Items (2022, methodology): “The KIT object models database [14] uses a setup similar to GSO to capture 145 scanned objects with calibrated images and grasp data for commercial manipulators; however, this scanner does not capture object bases.”
A Survey of Research on Cloud Robotics and Automation (2015, background): “New approaches are required that are robust to dirty data.”
Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards (2016, methodology): “0 contains 13,252 3D mesh models: 8,987 from the SHREC 2014 challenge dataset [28], 2,539 from ModelNet40 [45], 1,371 from 3DNet [44], 129 from the KIT object database∗ [19], 120 from BigBIRD∗ [38], 80 from the Yale-CMU-Berkeley dataset∗ [8], and 26 from the Amazon”
Dex-Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning (2018, methodology): “Our GQ-CNN trained on synthetic data from 3DNet [33], KIT [15], and the Adversarial objects.”
Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocols (2015, methodology): “5 The KIT object models database [19] 2012 Mesh with texture, stereo images Recognition, localization and manipulation 100 No http://i61p109.”
RGBD Datasets: Past, Present and Future (2016, background): “KIT object database [54] Minolta Vi-900 and stereo pair >100 XX ’12”