{"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/improving-annotation-for-3d-pose-dataset-of","title":"Improving Annotation for 3D Pose Dataset of Fine-Grained Object Categories","arxiv_id":"1810.09263","date":"2018-10-19","proceeding":null,"authors":["Yaming Wang","Xiao Tan","Yi Yang","Ziyu Li","Xiao Liu","Feng Zhou","Larry S. Davis"],"abstract":"Existing 3D pose datasets of object categories are limited to generic object\ntypes and lack of fine-grained information. In this work, we introduce a new\nlarge-scale dataset that consists of 409 fine-grained categories and 31,881\nimages with accurate 3D pose annotation. Specifically, we augment three\nexisting fine-grained object recognition datasets (StanfordCars, CompCars and\nFGVC-Aircraft) by finding a specific 3D model for each sub-category from\nShapeNet and manually annotating each 2D image by adjusting a full set of 7\ncontinuous perspective parameters. Since the fine-grained shapes allow 3D\nmodels to better fit the images, we further improve the annotation quality by\ninitializing from the human annotation and conducting local search of the pose\nparameters with the objective of maximizing the IoUs between the projected mask\nand the segmentation reference estimated from state-of-the-art deep\nConvolutional Neural Networks (CNNs). We provide full statistics of the\nannotations with qualitative and quantitative comparisons suggesting that our\ndataset can be a complementary source for studying 3D pose estimation. The\ndataset can be downloaded at http://users.umiacs.umd.edu/~wym/3dpose.html.","url_abs":"http://arxiv.org/abs/1810.09263v1","url_pdf":"http://arxiv.org/pdf/1810.09263v1.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":[{"paper_slug":"improving-annotation-for-3d-pose-dataset-of","repo_url":"https://github.com/yangyi02/3d_pose_fine_grained","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"improving-annotation-for-3d-pose-dataset-of","repo_url":"https://github.com/yangyi02/finegrained-pose","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"3d-pose-estimation","task_name":"3D Pose Estimation"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-recognition","task_name":"Object Recognition"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[{"slug":"fine-grained-3d-pose","name":"Fine-grained 3D Pose","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1810.09263","atlas_url":"https://app.syntology.ai/?focus=1810.09263","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}