{"url":"/dataset/multiviewx","name":"MultiviewX","full_name":null,"description_markdown":"**MultiviewX** is a synthetic Multiview pedestrian detection dataset. It is build using pedestrian models from PersonX, in Unity.\nThe MultiviewX dataset covers a square of 16 meters by 25 meters. The ground plane is quantized into a 640x1000 grid. There are 6 cameras with overlapping field-of-view in the MultiviewX dataset, each of which outputs a 1080x1920 resolution image. On average, 4.41 cameras are covering the same location.\n\nSource: [https://github.com/hou-yz/MVDet](https://github.com/hou-yz/MVDet)\nImage Source: [https://github.com/hou-yz/MVDet](https://github.com/hou-yz/MVDet)","description_withheld":null,"homepage":"https://github.com/hou-yz/MVDet","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/multiview-detection-with-feature-perspective","title":"Multiview Detection with Feature Perspective Transformation","first_author":"Yunzhong Hou","url":null},"license":null,"modalities":[],"tasks":[{"name":"Multi-Object Tracking","url":"/task/multi-object-tracking","datasets_with_task":"/datasets/task/multi-object-tracking"},{"name":"Multiview Detection","url":"/task/multiview-detection","datasets_with_task":"/datasets/task/multiview-detection"}],"languages":[],"variants":["MultiviewX"],"data_loaders":[{"repo":"https://github.com/hou-yz/MVDet","url":"https://github.com/hou-yz/MVDet","frameworks":["pytorch"]}],"num_papers_in_archive":26,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/multiview-detection-on-multiviewx","task":"Multiview Detection","dataset_variant":"MultiviewX","rows":9,"metrics":["MODA","MODP","Recall"],"first_row_in_archive_order":{"model":"M-MVOT","paper":"/paper/mahalanobis-distance-based-multi-view-optimal","metrics":{"MODA":"96.7","MODP":"86.1","Recall":"97.9"},"code_links":[{"title":"zqyq/Mahalanobis-Distance-based-Multi-view-Optimal-Transport-for-Multi-view-Crowd-Localization","url":"https://github.com/zqyq/Mahalanobis-Distance-based-Multi-view-Optimal-Transport-for-Multi-view-Crowd-Localization"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/multi-object-tracking-on-multiviewx","task":"Multi-Object Tracking","dataset_variant":"MultiviewX","rows":2,"metrics":["IDF1","MOTA"],"first_row_in_archive_order":{"model":"TrackTacular (Bilinear Sampling)","paper":"/paper/lifting-multi-view-detection-and-tracking-to","metrics":{"IDF1":"85.6","MOTA":"92.4"},"code_links":[{"title":"tteepe/tracktacular","url":"https://github.com/tteepe/tracktacular"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/mahalanobis-distance-based-multi-view-optimal","title":"Mahalanobis Distance-based Multi-view Optimal Transport for Multi-view Crowd Localization","date":"2024-09-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/lifting-multi-view-detection-and-tracking-to","title":"Lifting Multi-View Detection and Tracking to the Bird's Eye View","date":"2024-03-19","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/enhancing-multi-view-pedestrian-detection","title":"Enhancing Multi-View Pedestrian Detection Through Generalized 3D Feature Pulling","date":"2023-12-20","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/earlybird-early-fusion-for-multi-view","title":"EarlyBird: Early-Fusion for Multi-View Tracking in the Bird's Eye View","date":"2023-10-20","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/3d-random-occlusion-and-multi-layer","title":"3D Random Occlusion and Multi-Layer Projection for Deep Multi-Camera Pedestrian Localization","date":"2022-07-22","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":2,"samples_unverified":6,"pointer_only_for_licence":8,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/multiview-detection-with-shadow-transformer","title":"Multiview Detection with Shadow Transformer (and View-Coherent Data Augmentation)","date":"2021-08-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/stacked-homography-transformations-for-multi","title":"Stacked Homography Transformations for Multi-View Pedestrian Detection","date":"2021-01-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/multiview-detection-with-feature-perspective","title":"Multiview Detection with Feature Perspective Transformation","date":"2020-07-14","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deep-occlusion-reasoning-for-multi-camera","title":"Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection","date":"2017-04-19","rows_on_this_dataset":1,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":9,"samples_ran":2,"samples_unverified":7,"pointer_only_for_licence":9,"papers_with_no_sample_that_ran":1,"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."}