{"url":"/dataset/pascal3d-2","name":"PASCAL3D+","full_name":null,"description_markdown":"The Pascal3D+ multi-view dataset consists of images in the wild, i.e., images of object categories exhibiting high variability, captured under uncontrolled settings, in cluttered scenes and under many different poses. Pascal3D+ contains 12 categories of rigid objects selected from the PASCAL VOC 2012 dataset. These objects are annotated with pose information (azimuth, elevation and distance to camera). Pascal3D+ also adds pose annotated images of these 12 categories from the ImageNet dataset.\r\n\r\nSource: [Convolutional Models for Joint Object Categorization and Pose Estimation](https://arxiv.org/abs/1511.05175)\r\nImage Source: [Beyond PASCAL: A benchmark for 3D object detection in the wild](https://doi.org/10.1109/WACV.2014.6836101)","description_withheld":null,"homepage":"https://cvgl.stanford.edu/projects/pascal3d.html","introduced_date":"2014-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"Beyond PASCAL: A benchmark for 3D object detection in the wild","first_author":null,"url":"https://doi.org/10.1109/WACV.2014.6836101"},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"3D","url":"/datasets/modality/3d"}],"tasks":[{"name":"Object Detection","url":"/task/object-detection","datasets_with_task":"/datasets/task/object-detection"},{"name":"Pose Estimation","url":"/task/pose-estimation","datasets_with_task":"/datasets/task/pose-estimation"},{"name":"Keypoint Detection","url":"/task/keypoint-detection","datasets_with_task":"/datasets/task/keypoint-detection"},{"name":"Viewpoint Estimation","url":"/task/viewpoint-estimation","datasets_with_task":"/datasets/task/viewpoint-estimation"}],"languages":[],"variants":[" Pascal3D+","PASCAL3D+"],"data_loaders":[],"num_papers_in_archive":237,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/keypoint-detection-on-pascal3d","task":"Keypoint Detection","dataset_variant":"Pascal3D+","rows":4,"metrics":["Mean PCK"],"first_row_in_archive_order":{"model":"ConvNet + deformable shape model","paper":"/paper/6-dof-object-pose-from-semantic-keypoints","metrics":{"Mean PCK":"82.5"},"code_links":[{"title":"geopavlakos/object3d","url":"https://github.com/geopavlakos/object3d"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/starmap-for-category-agnostic-keypoint-and","title":"StarMap for Category-Agnostic Keypoint and Viewpoint Estimation","date":"2018-03-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/6-dof-object-pose-from-semantic-keypoints","title":"6-DoF Object Pose from Semantic Keypoints","date":"2017-03-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/viewpoints-and-keypoints","title":"Viewpoints and Keypoints","date":"2014-11-22","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/do-convnets-learn-correspondence","title":"Do Convnets Learn Correspondence?","date":"2014-11-04","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}