Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/40304
Title: | Dataset of Industrial Metal Objects | Data Creator - person: | De Roovere, Peter MOONEN, Steven MICHIELS, Nick Wyffels, Francis |
Data Creator - organization: | Ghent University Hasselt University |
Data Curator - person: | De Roovere, Peter MOONEN, Steven |
Data Curator - organization: | Ghent University Hasselt University |
Rights Holder - person: | De Roovere, Peter MOONEN, Steven |
Rights Holder - organization: | Ghent University Hasselt University |
Publisher: | Github | Issue Date: | 2022 | Abstract: | We present a diverse dataset of industrial metal objects. These objects are symmetric, textureless and highly reflective, leading to challenging conditions not captured in existing datasets. Our 6D object pose estimation dataset contains both real-world and synthetic images. Real-world data is obtained by recording multi-view images of scenes with varying object shapes, materials, carriers, compositions and lighting conditions. This leads to over 30,000 images, accurately labelled using a new public tool. Synthetic data is obtained by carefully simulating real-world conditions and varying them in a controlled and realistic way. This leads to over 500,000 synthetic images. The close correspondence between synthetic and real-world data, and controlled variations, will facilitate sim-to-real research. Our dataset's size and challenging nature will facilitate research on various computer vision tasks involving reflective materials. | Research Discipline: | Natural sciences > Information and computing sciences > Visual computing > Computer vision (01020902) Natural sciences > Information and computing sciences > Visual computing > Computer graphics (01020901) |
Keywords: | Synthetic Data;Machine Learning;Computer Vision;SIM2REAL;Graphics;Deep learning;digital twin | Link to publication/dataset: | https://pderoovere.github.io/dimo/ | Source: | Github. https://pderoovere.github.io/dimo/ | Publications related to the dataset: | 10.48550/arXiv.2208.04052 | License: | Creative Commons Attribution 4.0 International (CC-BY-4.0) | Access Rights: | Open Access | Category: | DS | Type: | Dataset |
Appears in Collections: | Datasets |
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