{"as_of":"2026-08-13T13:29:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:03e2db90bf0ea13c604175e07d66a5beaa870af2d7aa38244137f13a4404452a","coverage":[{"denominator":32,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":32,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T12:29:09.086070Z","state":"measured"},{"denominator":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2411.17174/citation-record","integrity":"/paper/2411.17174/integrity","json":"/paper/2411.17174/citation-record.json","paper":"/paper/2411.17174"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.448082Z","title":"Slow flow: Exploiting high-speed cameras for accurate and diverse optical flow reference data,","venue":null,"work_id":"9233d433-bf43-4dcf-94c5-8f2f55942261","year":2017},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:08.969891Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:37d6d17e1691aa6ef3785dec030900f31a953d8f5595992357caa6ed043ac232","observation_id":"f96bccd9-d969-48c7-b2e8-e080c3c8ee64","resolution":{"observed_at":"2026-08-12T12:29:09.452643Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.435824Z","title":"Learning analysis-by-synthesis for 6D pose estimation in RGB-D images,","venue":null,"work_id":"5dcca8e3-ab37-4036-8648-b8519efe95d1","year":2015},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:08.974255Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:c7da6aa9ade810557eeb6443c568d3a3dbc136bff4769128e6d41623d3563192","observation_id":"a3860cc6-c52a-4600-a3cc-622d742e98e6","resolution":{"observed_at":"2026-08-12T12:29:09.440592Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.424388Z","title":"Posecnn: a convolu- tional neural network for 6D object pose estimation in cluttered scenes,","venue":null,"work_id":"da08feda-5b41-4380-954a-61ef6d02de76","year":2018},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:08.978146Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:2c529b1c8aa782b90a4c07ad0509eeed522bb7eab4a66103b406cb60bba62813","observation_id":"95f842cc-c6de-4418-bb0c-8e5577856d86","resolution":{"observed_at":"2026-08-12T12:29:09.428499Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.413510Z","title":"Bop challenge 2022 on detection, segmentation and pose estimation of specific rigid objects,","venue":null,"work_id":"21ed2316-a3b4-416c-8814-40f366600244","year":2022},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:08.981881Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:f17417ae2fdebf6b6e86cd5c8f8b4455541a62498a7c42b77a49fa0e87003cb3","observation_id":"adaeb3b5-d7ba-4652-a768-565cb83fd5d4","resolution":{"observed_at":"2026-08-12T12:29:09.417158Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.402724Z","title":"A method for registration of 3-D shapes,","venue":null,"work_id":"01cb1298-f845-43c7-841f-ac830cda26be","year":1992},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:08.985525Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:dedab5f8a91b260725509fa035a8f3c7ffb1f56d63af445db6d37f922af131fc","observation_id":"fb3c99db-b507-40c9-91c2-aa84297a642a","resolution":{"observed_at":"2026-08-12T12:29:09.406324Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:08.989415Z","title":"Deepim: Deep iterative matching for 6d pose estimation,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:08.989415Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:c7a18b9278bd308383143f8d228ac439442a1362c45274883f96505873b63ca1","observation_id":"3d1373b1-2cc8-4fa6-bb73-2a295e3ead22","resolution":{"observed_at":"2026-08-12T12:29:08.989415Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.384100Z","title":"Cosypose: Consistent multi-view multi-object 6D pose estimation,","venue":null,"work_id":"203e9508-ec48-4e01-b202-b5c9c765243d","year":2020},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:08.993535Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:54ade8c150b8152483062fe695cfd10e861e1d5e246c63a3ba20b2a9ecc71c28","observation_id":"8a8e3df3-fed6-4701-b23c-9f59861b96ea","resolution":{"observed_at":"2026-08-12T12:29:09.388432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.372354Z","title":"Perspective flow aggregation for data-limited 6d object pose estimation,","venue":null,"work_id":"eaf12040-c700-4a3f-86a1-a8eea6383e3b","year":2022},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:08.997499Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:74d7e5dd7ae3eca26d20ba8a42a2ca197aba889d7a36cfecf6df9cacd9da3071","observation_id":"0ece01e8-c259-413a-8a36-185aa3f504b3","resolution":{"observed_at":"2026-08-12T12:29:09.376752Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.000989Z","title":"Raft: Recurrent all-pairs field transforms for op- tical flow,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:09.000989Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:346e1faa81763a4af6eea223ea3657d31514089541cf32c0351860fb4f84c275","observation_id":"1927534f-fde8-4637-b01b-c11926b70d89","resolution":{"observed_at":"2026-08-12T12:29:09.000989Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.355823Z","title":"Shape-constraint recurrent flow for 6D object pose estimation,","venue":null,"work_id":"e2b2dd3b-05d9-4dc9-8fa8-74641e39bb48","year":2023},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:09.005409Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:a34b923f92d456a35b1ed62a0356286339022e1f3aa9edf3a8ea2228f901ec85","observation_id":"aa6dc4fe-810d-46f2-b2ca-01766783d243","resolution":{"observed_at":"2026-08-12T12:29:09.359452Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.08501","last_updated":"2020-07-16T17:53:02Z","snapshot_observed_at":"2026-07-06T09:38:53.228850Z","submitted_at":"2020-07-16T17:53:02Z","title":"Accelerating 3D Deep Learning with PyTorch3D","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.08501","snapshot_observed_at":"2026-08-12T12:29:09.009099Z","title":"Accelerating 3D deep learning with Pytorch3D,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:09.009099Z"},"links":{"cited_paper":"/paper/2007.08501","citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:c5351b8f55b5905653e7040972ac823ffb7d65b65a6f4886067da4ed5f5b8739","observation_id":"61c4f420-fc71-4951-b900-554ea5f83ee5","resolution":{"observed_at":"2026-08-12T12:29:09.009099Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.344857Z","title":"Using relaxation to find a puppet,","venue":null,"work_id":"3183a2c2-4a78-4e30-a2bf-bcfaacff9af5","year":1976},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:09.013128Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:3060a135b95f207fbab45e433bedb32a47a8e2a0031497e99b51bc3ee552260f","observation_id":"03bfcc0d-fc1d-496f-8074-aca3cc88abb0","resolution":{"observed_at":"2026-08-12T12:29:09.348663Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.016969Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:09.016969Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:50c4ccf2f8e8316d16e0691db4f5f196b5d068d42db6ac6612717bcb6aea4ac5","observation_id":"3cee1b97-a3ae-46f4-9f52-0aac9da322ca","resolution":{"observed_at":"2026-08-12T12:29:09.016969Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.326154Z","title":"Learning to esti- mate hidden motions with global motion aggregation,","venue":null,"work_id":"290e05d5-4e8e-451e-b976-a5253e31310a","year":2021},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:09.020901Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:6621cab9c0243bbfcdac4da98b03a0851d378f203e740a38a310cffff9364c36","observation_id":"35eb832d-a56d-4def-abfd-2cf009c12c28","resolution":{"observed_at":"2026-08-12T12:29:09.330376Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.315447Z","title":"Swiftformer: Efficient additive attention for transformer-based real-time mobile vision applications,","venue":null,"work_id":"7a31a6dd-d71f-4a46-898f-fdaf90b11083","year":2023},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:09.024285Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:7bc42300f92c2bab5dfbd757e45679b4be0e767f84b621f3b3ade13e2adb726f","observation_id":"dd28ee59-b23a-4af3-b386-932519ee5b6d","resolution":{"observed_at":"2026-08-12T12:29:09.319400Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1409.1259","last_updated":"2014-10-07T18:08:30Z","snapshot_observed_at":"2026-07-06T03:53:24.366023Z","submitted_at":"2014-09-03T21:03:41Z","title":"On the Properties of Neural Machine Translation: Encoder-Decoder Approaches","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.1259","snapshot_observed_at":"2026-08-12T12:29:09.027857Z","title":"On the properties of neural machine translation: Encoder-decoder approaches,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:09.027857Z"},"links":{"cited_paper":"/paper/1409.1259","citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:0c7a71414496c8ece4d201c89a2f2d137545e7b5e6ef4ad72027569624102306","observation_id":"95c0b552-6e2b-40c3-9b12-a9164ee7b21b","resolution":{"observed_at":"2026-08-12T12:29:09.027857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.303989Z","title":"Gdr-net: Geometry- guided direct regression network for monocular 6D object pose estima- tion,","venue":null,"work_id":"cf93212e-f94e-4d4a-a59c-d62f6bb3475d","year":2021},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:09.032291Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:db4996f152e563fd2828ee92cb7be7e3f95ab3fe5690549e19eac18d9a03dfc1","observation_id":"be9ab045-9645-45f1-a036-acb1d8d5e227","resolution":{"observed_at":"2026-08-12T12:29:09.308059Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.292056Z","title":"Poet: Pose estimation transformer for single-view, multi-object 6D pose estimation,","venue":null,"work_id":"fe4fd1f7-bd5b-4f8e-80e2-a7a0c891e88b","year":2023},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:09.035705Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:a7db9c98ba107b6bc6c742dd2cedcc9e411eee4d6ecd5fb8340bfddb0e643872","observation_id":"f5766d6c-cab8-4391-8206-0ac51a97b0a6","resolution":{"observed_at":"2026-08-12T12:29:09.295986Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.280486Z","title":"Pvnet: Pixel- wise voting network for 6DoF pose estimation,","venue":null,"work_id":"3e4c2fe1-907b-48d9-94e7-16f4bcf1b103","year":2019},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:09.039312Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:26b2cedc64417aa38fabad1197516febc54c13b25d0e64a252799b467aef1cec","observation_id":"823d0423-3ad4-40e5-948f-4ac900a56fbd","resolution":{"observed_at":"2026-08-12T12:29:09.284077Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.269707Z","title":"High-resolution representation ob- ject pose estimation from monocular images,","venue":null,"work_id":"acdc259f-6e7b-4535-8bd2-6bc83eb0519d","year":2021},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:09.042838Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:44800e8ce276135edf9c73b75ad73bf317beae0b902508cd6962f7882a6fae3b","observation_id":"aa3d097c-dcb5-4086-b2f4-0816d52c3c63","resolution":{"observed_at":"2026-08-12T12:29:09.273545Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.257529Z","title":"Dpodv2: Dense correspondence- based 6 DoF pose estimation,","venue":null,"work_id":"6c2d9066-e10e-4a4e-9a3f-b2d5e74f4f7d","year":2021},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:09.046650Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:4bf5968bde233e5d2b106af7d806fde9492536a8f65045f16337f904eca8fcf6","observation_id":"29521977-0d9f-4584-8d8c-aace422f4d98","resolution":{"observed_at":"2026-08-12T12:29:09.261991Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.245320Z","title":"Surfemb: Dense and continuous correspondence distributions for object pose estimation with learnt surface embeddings,","venue":null,"work_id":"ab3e68a6-e569-4010-9581-1e7922edacc2","year":2022},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:09.050379Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:ba92e23a3c88db2a56ac46db7fa2e2de01704b02b4e1a297407b5e40248475e1","observation_id":"9e442cc0-e282-48d5-89d6-b03cd3787e05","resolution":{"observed_at":"2026-08-12T12:29:09.249944Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.234147Z","title":"Epnp: An accurate o(n) solution to the PnP problem,","venue":null,"work_id":"1abc3a5b-ff25-4bb3-a9c1-607d74cd95ac","year":2009},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:09.053995Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:fc3a1d7b104ff45433ed9ab03be9c456d69a73b4e9b517a8df831b96b38aa1f8","observation_id":"82cdfbde-6400-48d9-994b-df6b85b02d21","resolution":{"observed_at":"2026-08-12T12:29:09.238092Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.222826Z","title":"Epro-pnp: Generalized end-to-end probabilistic perspective-n-points for monocular object pose estimation,","venue":null,"work_id":"c5fd5af1-e7b9-4170-89c0-9d13933ea91c","year":2022},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:09.057499Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:077ada41ff0429a308db0a8c9c5b94f64b88a8437d21d0de81441b1a2f0b580f","observation_id":"70f93bbe-3fc6-4d3f-8c3d-f38b45cadfb2","resolution":{"observed_at":"2026-08-12T12:29:09.227015Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.210843Z","title":"Rnnpose: Recurrent 6- Dof object pose refinement with robust correspondence field estimation and pose optimization,","venue":null,"work_id":"882271c3-dcf2-4f77-acdf-ddd05d19d682","year":2022},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:09.061018Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:907f28e6bbcfe5d669ae80006341010cbc1d0f890b0ac29b76e044f9704789fe","observation_id":"20672f25-055e-467c-89a0-c9b1067b73cb","resolution":{"observed_at":"2026-08-12T12:29:09.214836Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.199249Z","title":"Repose: Fast 6D object pose refinement via deep texture rendering,","venue":null,"work_id":"7a4fba5f-a5c3-4f00-8eb5-015d44a2e27d","year":2021},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:09.064276Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:efde04748bdd2ead105fb916cde3a1e2c0495c4678a8332c58810c5dd051cc9d","observation_id":"100faae3-ee36-4f8a-b42b-02daff7f2363","resolution":{"observed_at":"2026-08-12T12:29:09.203037Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.067829Z","title":"Super-convergence: Very fast training of neural networks using large learning rates,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:09.067829Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:e9d8857305dc454f61f45a80859074d76973cc56062c55fa0231e24200f0bbdd","observation_id":"1654050d-b135-4257-b71c-367ba558af00","resolution":{"observed_at":"2026-08-12T12:29:09.067829Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.180631Z","title":"So-pose: Exploiting self-occlusion for direct 6D pose estimation,","venue":null,"work_id":"545cb177-2fff-431d-8286-d2555a6ebf79","year":2021},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:09.071372Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:00220b325713e8f827da85fd394cbbeaa6400e0f750790ba03afb30979da7aef","observation_id":"f9eb8aeb-2378-430a-91a4-7b62d372c4bf","resolution":{"observed_at":"2026-08-12T12:29:09.184652Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.169564Z","title":"Coupled iterative refinement for 6D multi-object pose estimation,","venue":null,"work_id":"dd0601f4-81d9-4d55-8c84-f8e22ec3833c","year":2022},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:09.075063Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:b21e837e9b8858978a879c88f7c4b31431fcf2efac80c398588b7286ff7e803e","observation_id":"61f42fbb-57a5-4616-9839-06247c4a1fa1","resolution":{"observed_at":"2026-08-12T12:29:09.173466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.157823Z","title":"Sc6d: Symmetry-agnostic and correspondence-free 6D object pose estimation,","venue":null,"work_id":"04a04a01-b771-40af-b80e-b030124924e6","year":2022},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:09.078727Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:4fc1e23956288de1b1c4b1be808bb3bf6eeead273089fe83b4f0e3e4b45148d7","observation_id":"9993b587-af4e-43c7-8c2f-24ef7d01f578","resolution":{"observed_at":"2026-08-12T12:29:09.161740Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.146784Z","title":"Bop challenge 2020 on 6D object localization,","venue":null,"work_id":"433fa5dd-a5b9-4004-bde4-3866dff33b26","year":2020},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:09.082429Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:cf427981a4740bb29e9dccc2bedd5ee6579659728c6d0d00230d65557452d308","observation_id":"61785610-31f0-43ee-85c9-d1c4d63cbefa","resolution":{"observed_at":"2026-08-12T12:29:09.150386Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:29:09.133503Z","title":"Wide-depth- range 6D object pose estimation in space,","venue":null,"work_id":"75f07c4a-9331-4be8-9eeb-61c556d96b32","year":2021},"citing_paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T12:29:09.086070Z"},"links":{"citing_paper":"/paper/2411.17174"},"observation_digest":"sha256:18309720b7b11d71d634617458ae90961854be62503ae933d9aab244feeb3387","observation_id":"e9e08de7-412b-4892-b9c6-07681300d713","resolution":{"observed_at":"2026-08-12T12:29:09.139090Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2411.17174","last_updated":"2024-11-26T07:28:48Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-13T08:17:56.082853Z","submitted_at":"2024-11-26T07:28:48Z","title":"GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation"},"reference_resolution":{"displayed":32,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":0,"verified_fuzzy":26},"total_outbound_references":32},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2411.17174."}