{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:LCKMOI3J3WPP3JHTJJR2ZYLOOO","short_pith_number":"pith:LCKMOI3J","canonical_record":{"source":{"id":"2209.08896","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-09-19T10:04:38Z","cross_cats_sorted":[],"title_canon_sha256":"d8d310069da00686b2167f0d88ec16509f2e7354c12da543638c73f0b8b2999c","abstract_canon_sha256":"ddc62af51f7d707fc997f7abe4de7e878d55b5998a6838c60235e8941a18453a"},"schema_version":"1.0"},"canonical_sha256":"5894c72369dd9efda4f34a63ace16e73a6b8de7f5e5ac1b4f4e173c1e8ae0d09","source":{"kind":"arxiv","id":"2209.08896","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2209.08896","created_at":"2026-07-05T04:58:47Z"},{"alias_kind":"arxiv_version","alias_value":"2209.08896v1","created_at":"2026-07-05T04:58:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.08896","created_at":"2026-07-05T04:58:47Z"},{"alias_kind":"pith_short_12","alias_value":"LCKMOI3J3WPP","created_at":"2026-07-05T04:58:47Z"},{"alias_kind":"pith_short_16","alias_value":"LCKMOI3J3WPP3JHT","created_at":"2026-07-05T04:58:47Z"},{"alias_kind":"pith_short_8","alias_value":"LCKMOI3J","created_at":"2026-07-05T04:58:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:LCKMOI3J3WPP3JHTJJR2ZYLOOO","target":"record","payload":{"canonical_record":{"source":{"id":"2209.08896","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-09-19T10:04:38Z","cross_cats_sorted":[],"title_canon_sha256":"d8d310069da00686b2167f0d88ec16509f2e7354c12da543638c73f0b8b2999c","abstract_canon_sha256":"ddc62af51f7d707fc997f7abe4de7e878d55b5998a6838c60235e8941a18453a"},"schema_version":"1.0"},"canonical_sha256":"5894c72369dd9efda4f34a63ace16e73a6b8de7f5e5ac1b4f4e173c1e8ae0d09","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:58:47.403726Z","signature_b64":"7sZkzfH2kRTlNau2sn9TNh3EW24uZGTjQYcwpYExa5Xv+Vxw3Kl0M7R62iDQYaxzKnZl46FMS8b3+y1OumcWDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5894c72369dd9efda4f34a63ace16e73a6b8de7f5e5ac1b4f4e173c1e8ae0d09","last_reissued_at":"2026-07-05T04:58:47.403334Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:58:47.403334Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2209.08896","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:58:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/7KdToiLxTvpz5UTUDbkOAKqr1+0HB4A2WOhkpjgka7neO6+OP1J1y2fr58oUMAb0uHVrPlpRRoga4+PmxjYBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T22:48:00.030593Z"},"content_sha256":"05878db8f3d19387a07815455c9604913ceeb0c1122d85fae3e7202ece1933a2","schema_version":"1.0","event_id":"sha256:05878db8f3d19387a07815455c9604913ceeb0c1122d85fae3e7202ece1933a2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:LCKMOI3J3WPP3JHTJJR2ZYLOOO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"NeuralMarker: A Framework for Learning General Marker Correspondence","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guofeng Zhang, Hongsheng Li, Ka Chun Cheung, Weihong Pan, Weikang Bian, Xiaokun Pan, Yan Xu, Zhaoyang Huang","submitted_at":"2022-09-19T10:04:38Z","abstract_excerpt":"We tackle the problem of estimating correspondences from a general marker, such as a movie poster, to an image that captures such a marker. Conventionally, this problem is addressed by fitting a homography model based on sparse feature matching. However, they are only able to handle plane-like markers and the sparse features do not sufficiently utilize appearance information. In this paper, we propose a novel framework NeuralMarker, training a neural network estimating dense marker correspondences under various challenging conditions, such as marker deformation, harsh lighting, etc. Besides, w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.08896","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2209.08896/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:58:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EiZyfdoPSgnGqMR3Exl3JPaS9y1A6Gos5ssIu+qA6tYQkUqD7BuWtqZAVwaevXHvTyV/78GIyRdxH3SU/Xa3Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T22:48:00.031491Z"},"content_sha256":"d316c6ce52f1f891c16432a62deab84fe807938ca9917dec489019d0aee3f837","schema_version":"1.0","event_id":"sha256:d316c6ce52f1f891c16432a62deab84fe807938ca9917dec489019d0aee3f837"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LCKMOI3J3WPP3JHTJJR2ZYLOOO/bundle.json","state_url":"https://pith.science/pith/LCKMOI3J3WPP3JHTJJR2ZYLOOO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LCKMOI3J3WPP3JHTJJR2ZYLOOO/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-17T22:48:00Z","links":{"resolver":"https://pith.science/pith/LCKMOI3J3WPP3JHTJJR2ZYLOOO","bundle":"https://pith.science/pith/LCKMOI3J3WPP3JHTJJR2ZYLOOO/bundle.json","state":"https://pith.science/pith/LCKMOI3J3WPP3JHTJJR2ZYLOOO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LCKMOI3J3WPP3JHTJJR2ZYLOOO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:LCKMOI3J3WPP3JHTJJR2ZYLOOO","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"ddc62af51f7d707fc997f7abe4de7e878d55b5998a6838c60235e8941a18453a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-09-19T10:04:38Z","title_canon_sha256":"d8d310069da00686b2167f0d88ec16509f2e7354c12da543638c73f0b8b2999c"},"schema_version":"1.0","source":{"id":"2209.08896","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2209.08896","created_at":"2026-07-05T04:58:47Z"},{"alias_kind":"arxiv_version","alias_value":"2209.08896v1","created_at":"2026-07-05T04:58:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.08896","created_at":"2026-07-05T04:58:47Z"},{"alias_kind":"pith_short_12","alias_value":"LCKMOI3J3WPP","created_at":"2026-07-05T04:58:47Z"},{"alias_kind":"pith_short_16","alias_value":"LCKMOI3J3WPP3JHT","created_at":"2026-07-05T04:58:47Z"},{"alias_kind":"pith_short_8","alias_value":"LCKMOI3J","created_at":"2026-07-05T04:58:47Z"}],"graph_snapshots":[{"event_id":"sha256:d316c6ce52f1f891c16432a62deab84fe807938ca9917dec489019d0aee3f837","target":"graph","created_at":"2026-07-05T04:58:47Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2209.08896/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We tackle the problem of estimating correspondences from a general marker, such as a movie poster, to an image that captures such a marker. Conventionally, this problem is addressed by fitting a homography model based on sparse feature matching. However, they are only able to handle plane-like markers and the sparse features do not sufficiently utilize appearance information. In this paper, we propose a novel framework NeuralMarker, training a neural network estimating dense marker correspondences under various challenging conditions, such as marker deformation, harsh lighting, etc. Besides, w","authors_text":"Guofeng Zhang, Hongsheng Li, Ka Chun Cheung, Weihong Pan, Weikang Bian, Xiaokun Pan, Yan Xu, Zhaoyang Huang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-09-19T10:04:38Z","title":"NeuralMarker: A Framework for Learning General Marker Correspondence"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.08896","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:05878db8f3d19387a07815455c9604913ceeb0c1122d85fae3e7202ece1933a2","target":"record","created_at":"2026-07-05T04:58:47Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"ddc62af51f7d707fc997f7abe4de7e878d55b5998a6838c60235e8941a18453a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-09-19T10:04:38Z","title_canon_sha256":"d8d310069da00686b2167f0d88ec16509f2e7354c12da543638c73f0b8b2999c"},"schema_version":"1.0","source":{"id":"2209.08896","kind":"arxiv","version":1}},"canonical_sha256":"5894c72369dd9efda4f34a63ace16e73a6b8de7f5e5ac1b4f4e173c1e8ae0d09","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5894c72369dd9efda4f34a63ace16e73a6b8de7f5e5ac1b4f4e173c1e8ae0d09","first_computed_at":"2026-07-05T04:58:47.403334Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:58:47.403334Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7sZkzfH2kRTlNau2sn9TNh3EW24uZGTjQYcwpYExa5Xv+Vxw3Kl0M7R62iDQYaxzKnZl46FMS8b3+y1OumcWDA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:58:47.403726Z","signed_message":"canonical_sha256_bytes"},"source_id":"2209.08896","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:05878db8f3d19387a07815455c9604913ceeb0c1122d85fae3e7202ece1933a2","sha256:d316c6ce52f1f891c16432a62deab84fe807938ca9917dec489019d0aee3f837"],"state_sha256":"7def5c3683b24bdfd678c82c64aee98bac5d5e36ee8f459164e9160bcaab1476"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vGq4Nt41gGMdbrwHCOOp3L41o8nL+91hbu6CjpxyMpOUt35YVqChmY86cyVUovTXkB3JtW7Ty3TYGLIs/OtlDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T22:48:00.037726Z","bundle_sha256":"0965356cc7b4991b318891183860cc96ddff92d9dc9930ce0a81c01ff5dc6eb9"}}