{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:YTUHWDO75CF2V6APCBPFU2EBK2","short_pith_number":"pith:YTUHWDO7","canonical_record":{"source":{"id":"2011.02229","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-11-04T11:05:44Z","cross_cats_sorted":[],"title_canon_sha256":"0c47005de953c5593d258c8c1b1e254737e81246cc9aa28a49e09473d48f505d","abstract_canon_sha256":"6f5ac5833693c5f77a609ed553b978744c246849f469481253ec396f83010c39"},"schema_version":"1.0"},"canonical_sha256":"c4e87b0ddfe88baaf80f105e5a688156b8330e34e2ff9a5504a9ac706aa2a541","source":{"kind":"arxiv","id":"2011.02229","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2011.02229","created_at":"2026-07-05T01:49:10Z"},{"alias_kind":"arxiv_version","alias_value":"2011.02229v1","created_at":"2026-07-05T01:49:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.02229","created_at":"2026-07-05T01:49:10Z"},{"alias_kind":"pith_short_12","alias_value":"YTUHWDO75CF2","created_at":"2026-07-05T01:49:10Z"},{"alias_kind":"pith_short_16","alias_value":"YTUHWDO75CF2V6AP","created_at":"2026-07-05T01:49:10Z"},{"alias_kind":"pith_short_8","alias_value":"YTUHWDO7","created_at":"2026-07-05T01:49:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:YTUHWDO75CF2V6APCBPFU2EBK2","target":"record","payload":{"canonical_record":{"source":{"id":"2011.02229","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-11-04T11:05:44Z","cross_cats_sorted":[],"title_canon_sha256":"0c47005de953c5593d258c8c1b1e254737e81246cc9aa28a49e09473d48f505d","abstract_canon_sha256":"6f5ac5833693c5f77a609ed553b978744c246849f469481253ec396f83010c39"},"schema_version":"1.0"},"canonical_sha256":"c4e87b0ddfe88baaf80f105e5a688156b8330e34e2ff9a5504a9ac706aa2a541","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:49:10.682551Z","signature_b64":"pt9umXuj5yLsybeCWczKmGsUwDDbaKmGPXuV3HcVQ8fmqXGekJj1FbVtVfVXENIvqMaFUUlaQ1olVUmbj2mlBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c4e87b0ddfe88baaf80f105e5a688156b8330e34e2ff9a5504a9ac706aa2a541","last_reissued_at":"2026-07-05T01:49:10.682125Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:49:10.682125Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2011.02229","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":"2026-07-05T01:49:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9ku5yDfa9UmcW3Zkeg7x/uk4A6n65l9ndsld8E/1htv/zKGmY6NcSrToOZGq7evHtyRI/FKsyPcprcz1VvctAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T06:49:58.444530Z"},"content_sha256":"abc8a72d171dc4d52f2f96e446a099e0adf26e8b430874e54809cf21a65637ae","schema_version":"1.0","event_id":"sha256:abc8a72d171dc4d52f2f96e446a099e0adf26e8b430874e54809cf21a65637ae"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:YTUHWDO75CF2V6APCBPFU2EBK2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Registration Loss Learning for Deep Probabilistic Point Set Registration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Felix J\\\"aremo Lawin, Per-Erik Forss\\'en","submitted_at":"2020-11-04T11:05:44Z","abstract_excerpt":"Probabilistic methods for point set registration have interesting theoretical properties, such as linear complexity in the number of used points, and they easily generalize to joint registration of multiple point sets. In this work, we improve their recognition performance to match state of the art. This is done by incorporating learned features, by adding a von Mises-Fisher feature model in each mixture component, and by using learned attention weights. We learn these jointly using a registration loss learning strategy (RLL) that directly uses the registration error as a loss, by back-propaga"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.02229","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/2011.02229/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":"2026-07-05T01:49:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LKvbWwLHPSV9lJQRIvj/ME0ifP8N+RRUPZ5xLDxKBO3Ac4yTPZgdKxeP0TKqs6580+RBZunw1UIp8oYH7ufRCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T06:49:58.444913Z"},"content_sha256":"a8acafb50a21264c789ddc24e46438990f3807bfeaf3c9975e27cc2e8dfa02e8","schema_version":"1.0","event_id":"sha256:a8acafb50a21264c789ddc24e46438990f3807bfeaf3c9975e27cc2e8dfa02e8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YTUHWDO75CF2V6APCBPFU2EBK2/bundle.json","state_url":"https://pith.science/pith/YTUHWDO75CF2V6APCBPFU2EBK2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YTUHWDO75CF2V6APCBPFU2EBK2/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":"2026-07-10T06:49:58Z","links":{"resolver":"https://pith.science/pith/YTUHWDO75CF2V6APCBPFU2EBK2","bundle":"https://pith.science/pith/YTUHWDO75CF2V6APCBPFU2EBK2/bundle.json","state":"https://pith.science/pith/YTUHWDO75CF2V6APCBPFU2EBK2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YTUHWDO75CF2V6APCBPFU2EBK2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:YTUHWDO75CF2V6APCBPFU2EBK2","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":"6f5ac5833693c5f77a609ed553b978744c246849f469481253ec396f83010c39","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-11-04T11:05:44Z","title_canon_sha256":"0c47005de953c5593d258c8c1b1e254737e81246cc9aa28a49e09473d48f505d"},"schema_version":"1.0","source":{"id":"2011.02229","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2011.02229","created_at":"2026-07-05T01:49:10Z"},{"alias_kind":"arxiv_version","alias_value":"2011.02229v1","created_at":"2026-07-05T01:49:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.02229","created_at":"2026-07-05T01:49:10Z"},{"alias_kind":"pith_short_12","alias_value":"YTUHWDO75CF2","created_at":"2026-07-05T01:49:10Z"},{"alias_kind":"pith_short_16","alias_value":"YTUHWDO75CF2V6AP","created_at":"2026-07-05T01:49:10Z"},{"alias_kind":"pith_short_8","alias_value":"YTUHWDO7","created_at":"2026-07-05T01:49:10Z"}],"graph_snapshots":[{"event_id":"sha256:a8acafb50a21264c789ddc24e46438990f3807bfeaf3c9975e27cc2e8dfa02e8","target":"graph","created_at":"2026-07-05T01:49:10Z","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/2011.02229/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Probabilistic methods for point set registration have interesting theoretical properties, such as linear complexity in the number of used points, and they easily generalize to joint registration of multiple point sets. In this work, we improve their recognition performance to match state of the art. This is done by incorporating learned features, by adding a von Mises-Fisher feature model in each mixture component, and by using learned attention weights. We learn these jointly using a registration loss learning strategy (RLL) that directly uses the registration error as a loss, by back-propaga","authors_text":"Felix J\\\"aremo Lawin, Per-Erik Forss\\'en","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-11-04T11:05:44Z","title":"Registration Loss Learning for Deep Probabilistic Point Set Registration"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.02229","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:abc8a72d171dc4d52f2f96e446a099e0adf26e8b430874e54809cf21a65637ae","target":"record","created_at":"2026-07-05T01:49:10Z","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":"6f5ac5833693c5f77a609ed553b978744c246849f469481253ec396f83010c39","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-11-04T11:05:44Z","title_canon_sha256":"0c47005de953c5593d258c8c1b1e254737e81246cc9aa28a49e09473d48f505d"},"schema_version":"1.0","source":{"id":"2011.02229","kind":"arxiv","version":1}},"canonical_sha256":"c4e87b0ddfe88baaf80f105e5a688156b8330e34e2ff9a5504a9ac706aa2a541","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c4e87b0ddfe88baaf80f105e5a688156b8330e34e2ff9a5504a9ac706aa2a541","first_computed_at":"2026-07-05T01:49:10.682125Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:49:10.682125Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pt9umXuj5yLsybeCWczKmGsUwDDbaKmGPXuV3HcVQ8fmqXGekJj1FbVtVfVXENIvqMaFUUlaQ1olVUmbj2mlBg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:49:10.682551Z","signed_message":"canonical_sha256_bytes"},"source_id":"2011.02229","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:abc8a72d171dc4d52f2f96e446a099e0adf26e8b430874e54809cf21a65637ae","sha256:a8acafb50a21264c789ddc24e46438990f3807bfeaf3c9975e27cc2e8dfa02e8"],"state_sha256":"3a83f61f78cb1fbeaac08da8fdb9b40b23e54c3785cead124737198246bf1bef"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"To9cjuy+MsArUfx+01RRPR7baRLnVPdomisJZH/3T2VcSMl6Qigd1+SfjtEuMf6JXF/vpak7n5IpaIAHWmKJCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-10T06:49:58.448153Z","bundle_sha256":"c5eb39d8469816b62400c81f1cecfa710128d15183647377355262e64a4d6930"}}