{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:DOM47ZVAMADF3HCGXKJ6ARTPHP","short_pith_number":"pith:DOM47ZVA","canonical_record":{"source":{"id":"1712.07629","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-20T18:38:35Z","cross_cats_sorted":[],"title_canon_sha256":"d4c5f13d82e824c9da9dcf1690e4903217aea8b216248909169f77a7be864396","abstract_canon_sha256":"dfaf9207f54bb7f080ffee12b2c0072811473a001a64d7fb3f9278a083098732"},"schema_version":"1.0"},"canonical_sha256":"1b99cfe6a060065d9c46ba93e0466f3bc61c64dc00b17e96e31eeacc76d234d8","source":{"kind":"arxiv","id":"1712.07629","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.07629","created_at":"2026-05-18T00:18:03Z"},{"alias_kind":"arxiv_version","alias_value":"1712.07629v4","created_at":"2026-05-18T00:18:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.07629","created_at":"2026-05-18T00:18:03Z"},{"alias_kind":"pith_short_12","alias_value":"DOM47ZVAMADF","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_16","alias_value":"DOM47ZVAMADF3HCG","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_8","alias_value":"DOM47ZVA","created_at":"2026-05-18T12:31:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:DOM47ZVAMADF3HCGXKJ6ARTPHP","target":"record","payload":{"canonical_record":{"source":{"id":"1712.07629","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-20T18:38:35Z","cross_cats_sorted":[],"title_canon_sha256":"d4c5f13d82e824c9da9dcf1690e4903217aea8b216248909169f77a7be864396","abstract_canon_sha256":"dfaf9207f54bb7f080ffee12b2c0072811473a001a64d7fb3f9278a083098732"},"schema_version":"1.0"},"canonical_sha256":"1b99cfe6a060065d9c46ba93e0466f3bc61c64dc00b17e96e31eeacc76d234d8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:18:03.853308Z","signature_b64":"USgKf8S9Q5iWdCSe4MmALTaV/TTAxNfeHaxYt5uTj8geP+H6ZLCsEPJCcAG4dmv1I4SVevP3yKUi4f8hnjlQDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1b99cfe6a060065d9c46ba93e0466f3bc61c64dc00b17e96e31eeacc76d234d8","last_reissued_at":"2026-05-18T00:18:03.852616Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:18:03.852616Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1712.07629","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:18:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EDYCz9oKMpiuJyV+7HpckXJ23V438EoIJz6FV1lCIxoRUy6tSA0/dShDPRpcjxjOL1SjXHlr09R13jgk2J72Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T13:03:27.669593Z"},"content_sha256":"b80312a58f74866ccc92c4a94776553e23b8a8000bb569854ab20726783449fb","schema_version":"1.0","event_id":"sha256:b80312a58f74866ccc92c4a94776553e23b8a8000bb569854ab20726783449fb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:DOM47ZVAMADF3HCGXKJ6ARTPHP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SuperPoint: Self-Supervised Interest Point Detection and Description","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrew Rabinovich, Daniel DeTone, Tomasz Malisiewicz","submitted_at":"2017-12-20T18:38:35Z","abstract_excerpt":"This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision. As opposed to patch-based neural networks, our fully-convolutional model operates on full-sized images and jointly computes pixel-level interest point locations and associated descriptors in one forward pass. We introduce Homographic Adaptation, a multi-scale, multi-homography approach for boosting interest point detection repeatability and performing cross-domain adaptation (e.g., synthetic-to-real). Our model,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.07629","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"2026-05-18T00:18:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yh1PJe5ZKTi5YA4McrABSuWA47wSfN6TTZJiK/4Cde2SP+x8kh8GWfRE3Pnk+3UK3fK1E2N9zw2J4MCLTY1QDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T13:03:27.669937Z"},"content_sha256":"2da6c8a047e04e45e03b41d35452ba54a0603a46acd4cc8857f356ca8bd1aa30","schema_version":"1.0","event_id":"sha256:2da6c8a047e04e45e03b41d35452ba54a0603a46acd4cc8857f356ca8bd1aa30"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DOM47ZVAMADF3HCGXKJ6ARTPHP/bundle.json","state_url":"https://pith.science/pith/DOM47ZVAMADF3HCGXKJ6ARTPHP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DOM47ZVAMADF3HCGXKJ6ARTPHP/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":"2026-05-20T13:03:27Z","links":{"resolver":"https://pith.science/pith/DOM47ZVAMADF3HCGXKJ6ARTPHP","bundle":"https://pith.science/pith/DOM47ZVAMADF3HCGXKJ6ARTPHP/bundle.json","state":"https://pith.science/pith/DOM47ZVAMADF3HCGXKJ6ARTPHP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DOM47ZVAMADF3HCGXKJ6ARTPHP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:DOM47ZVAMADF3HCGXKJ6ARTPHP","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":"dfaf9207f54bb7f080ffee12b2c0072811473a001a64d7fb3f9278a083098732","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-20T18:38:35Z","title_canon_sha256":"d4c5f13d82e824c9da9dcf1690e4903217aea8b216248909169f77a7be864396"},"schema_version":"1.0","source":{"id":"1712.07629","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.07629","created_at":"2026-05-18T00:18:03Z"},{"alias_kind":"arxiv_version","alias_value":"1712.07629v4","created_at":"2026-05-18T00:18:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.07629","created_at":"2026-05-18T00:18:03Z"},{"alias_kind":"pith_short_12","alias_value":"DOM47ZVAMADF","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_16","alias_value":"DOM47ZVAMADF3HCG","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_8","alias_value":"DOM47ZVA","created_at":"2026-05-18T12:31:12Z"}],"graph_snapshots":[{"event_id":"sha256:2da6c8a047e04e45e03b41d35452ba54a0603a46acd4cc8857f356ca8bd1aa30","target":"graph","created_at":"2026-05-18T00:18:03Z","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"},"paper":{"abstract_excerpt":"This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision. As opposed to patch-based neural networks, our fully-convolutional model operates on full-sized images and jointly computes pixel-level interest point locations and associated descriptors in one forward pass. We introduce Homographic Adaptation, a multi-scale, multi-homography approach for boosting interest point detection repeatability and performing cross-domain adaptation (e.g., synthetic-to-real). Our model,","authors_text":"Andrew Rabinovich, Daniel DeTone, Tomasz Malisiewicz","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-20T18:38:35Z","title":"SuperPoint: Self-Supervised Interest Point Detection and Description"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.07629","kind":"arxiv","version":4},"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:b80312a58f74866ccc92c4a94776553e23b8a8000bb569854ab20726783449fb","target":"record","created_at":"2026-05-18T00:18:03Z","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":"dfaf9207f54bb7f080ffee12b2c0072811473a001a64d7fb3f9278a083098732","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-20T18:38:35Z","title_canon_sha256":"d4c5f13d82e824c9da9dcf1690e4903217aea8b216248909169f77a7be864396"},"schema_version":"1.0","source":{"id":"1712.07629","kind":"arxiv","version":4}},"canonical_sha256":"1b99cfe6a060065d9c46ba93e0466f3bc61c64dc00b17e96e31eeacc76d234d8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1b99cfe6a060065d9c46ba93e0466f3bc61c64dc00b17e96e31eeacc76d234d8","first_computed_at":"2026-05-18T00:18:03.852616Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:18:03.852616Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"USgKf8S9Q5iWdCSe4MmALTaV/TTAxNfeHaxYt5uTj8geP+H6ZLCsEPJCcAG4dmv1I4SVevP3yKUi4f8hnjlQDw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:18:03.853308Z","signed_message":"canonical_sha256_bytes"},"source_id":"1712.07629","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b80312a58f74866ccc92c4a94776553e23b8a8000bb569854ab20726783449fb","sha256:2da6c8a047e04e45e03b41d35452ba54a0603a46acd4cc8857f356ca8bd1aa30"],"state_sha256":"02cabfd48c0fa9951ca3e0cbde02581684efa1bebbe9609b5f14425843ad8cc9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"88G5J+6wcs7SCcDSRn9clJzpQWrdY+Rt+N/RFq7XU0x+UZ5qMzkvQLFNdzMowMJj8oBz8yXl3jwaUkEW8zjZBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-20T13:03:27.672116Z","bundle_sha256":"97d91712df6283ca676eaa7a6904db54d041bd748dfae068cb1f73cbf54d0beb"}}