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• W4383108843 abstract "Robots operating in the real world are expected to detect, classify, segment, and estimate the pose of objects to accomplish their task. Modern approaches using deep learning not only require large volumes of data but also pixel-accurate annotations in order to evaluate the performance and therefore safety of these algorithms. At present, publicly available tools for annotating data are scarce and those that are available rely on depth sensors, which excludes their use for transparent, metallic, and general non-Lambertian objects. To address this issue, we present a novel method for creating valuable datasets that can be used in these more difficult cases. Our key contribution is a purely RGB-based scene-level annotation approach that uses a neural radiance field-based method to automatically align objects. A set of user studies demonstrates the accuracy and speed of our approach over a purely manual or depth sensor assisted pipeline. We provide an open-source implementation of each component and a ROS-based recorder for capturing data with a eye-in-hand robot system. Code will be made available at https://github.com/markus-suchi/3D-DAT." @default.
• W4383108843 created "2023-07-05" @default.
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• W4383108843 date "2023-05-29" @default.
• W4383108843 modified "2023-09-30" @default.
• W4383108843 title "3D-DAT: 3D-Dataset Annotation Toolkit for Robotic Vision" @default.
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• W4383108843 doi "https://doi.org/10.1109/icra48891.2023.10160669" @default.
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