{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/easylabel-a-semi-automatic-pixel-wise-object","title":"EasyLabel: A Semi-Automatic Pixel-wise Object Annotation Tool for Creating Robotic RGB-D Datasets","arxiv_id":"1902.01626","date":"2019-02-05","proceeding":null,"authors":["Markus Suchi","Timothy Patten","David Fischinger","Markus Vincze"],"abstract":"Developing robot perception systems for recognizing objects in the real-world\nrequires computer vision algorithms to be carefully scrutinized with respect to\nthe expected operating domain. This demands large quantities of ground truth\ndata to rigorously evaluate the performance of algorithms. This paper presents\nthe EasyLabel tool for easily acquiring high quality ground truth annotation of\nobjects at the pixel-level in densely cluttered scenes. In a semi-automatic\nprocess, complex scenes are incrementally built and EasyLabel exploits depth\nchange to extract precise object masks at each step. We use this tool to\ngenerate the Object Cluttered Indoor Dataset (OCID) that captures diverse\nsettings of objects, background, context, sensor to scene distance, viewpoint\nangle and lighting conditions. OCID is used to perform a systematic comparison\nof existing object segmentation methods. The baseline comparison supports the\nneed for pixel- and object-wise annotation to progress robot vision towards\nrealistic applications. This insight reveals the usefulness of EasyLabel and\nOCID to better understand the challenges that robots face in the real-world.\n  Copyright 20XX IEEE. Personal use of this material is permitted. Permission\nfrom IEEE must be obtained for all other uses, in any current or future media,\nincluding reprinting/republishing this material for advertising or promotional\npurposes, creating new collective works, for resale or redistribution to\nservers or lists, or reuse of any copyrighted component of this work in other\nworks.","url_abs":"http://arxiv.org/abs/1902.01626v2","url_pdf":"http://arxiv.org/pdf/1902.01626v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[{"slug":"ocid","name":"OCID","full_name":"Object Clutter Indoor Dataset"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1902.01626","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}