{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:MRPJTVDQIL3CFIPOTILLR7HU4T","short_pith_number":"pith:MRPJTVDQ","canonical_record":{"source":{"id":"1710.08135","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-23T08:12:45Z","cross_cats_sorted":[],"title_canon_sha256":"2a30c6a9dfe2973a6c0690bbe935823eae4b515cb0fa3de9844dc01cf70a093f","abstract_canon_sha256":"0f1e45abd724b196d31aab88eb26bd7d5d0e296fca23e1a11a7d6842ff781034"},"schema_version":"1.0"},"canonical_sha256":"645e99d47042f622a1ee9a16b8fcf4e4c40990f43f2a9a8601e17d5e6199808c","source":{"kind":"arxiv","id":"1710.08135","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.08135","created_at":"2026-05-18T00:32:17Z"},{"alias_kind":"arxiv_version","alias_value":"1710.08135v1","created_at":"2026-05-18T00:32:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.08135","created_at":"2026-05-18T00:32:17Z"},{"alias_kind":"pith_short_12","alias_value":"MRPJTVDQIL3C","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_16","alias_value":"MRPJTVDQIL3CFIPO","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_8","alias_value":"MRPJTVDQ","created_at":"2026-05-18T12:31:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:MRPJTVDQIL3CFIPOTILLR7HU4T","target":"record","payload":{"canonical_record":{"source":{"id":"1710.08135","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-23T08:12:45Z","cross_cats_sorted":[],"title_canon_sha256":"2a30c6a9dfe2973a6c0690bbe935823eae4b515cb0fa3de9844dc01cf70a093f","abstract_canon_sha256":"0f1e45abd724b196d31aab88eb26bd7d5d0e296fca23e1a11a7d6842ff781034"},"schema_version":"1.0"},"canonical_sha256":"645e99d47042f622a1ee9a16b8fcf4e4c40990f43f2a9a8601e17d5e6199808c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:32:17.101188Z","signature_b64":"z5JCQqH0iMaba0bZm/nHt+TrjLtz3M+5ke7g/aL3FfhUM87/0/dL+MsKkP2I3LSvwI7raH3ndQJwohf4DbOeDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"645e99d47042f622a1ee9a16b8fcf4e4c40990f43f2a9a8601e17d5e6199808c","last_reissued_at":"2026-05-18T00:32:17.100639Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:32:17.100639Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1710.08135","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":"2026-05-18T00:32:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xsu4WvvA7XnaDXEJmPTSyZw8XJ9prI4yuhKm4wIQ5cDHOl2dhnNkGXHykEPMQL9wyCgTPW90/lIGAl41bbmgAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T01:26:46.527630Z"},"content_sha256":"984bbc8d1c4e9fb1d25b9faadadc76baea67e1f36afb5114eef52c7e8b7272a5","schema_version":"1.0","event_id":"sha256:984bbc8d1c4e9fb1d25b9faadadc76baea67e1f36afb5114eef52c7e8b7272a5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:MRPJTVDQIL3CFIPOTILLR7HU4T","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An iterative closest point method for measuring the level of similarity of 3d log scans in wood industry","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cyrine Selma (CRAN), Hind Haouzi (CRAN), Jonathan Gaudreault, Michael Morin, Philippe Thomas (CRAN)","submitted_at":"2017-10-23T08:12:45Z","abstract_excerpt":"In the Canadian's lumber industry, simulators are used to predict the lumbers resulting from the sawing of a log at a given sawmill. Giving a log or several logs' 3D scans as input, simulators perform a real-time job to predict the lumbers. These simulators, however, tend to be slow at processing large volume of wood. We thus explore an alternative approximation techniques based on the Iterative Closest Point (ICP) algorithm to identify the already processed log to which an unseen log resembles the most. The main benefit of the ICP approach is that it can easily handle 3D scans with a variable"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.08135","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":""},"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":"2026-05-18T00:32:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+aaF7ODIwgXUVbWWbvy/Jf0ONStX58C0Ld1WtVZmAQGlogcCWZ2u3MnkF8gecg9B7AK/axUZRfphTEvy1wJkCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T01:26:46.528390Z"},"content_sha256":"9cc0e86bfa08f2aa4313dad30f452497d7ec27dfc079ced870df090ef1fb59c4","schema_version":"1.0","event_id":"sha256:9cc0e86bfa08f2aa4313dad30f452497d7ec27dfc079ced870df090ef1fb59c4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MRPJTVDQIL3CFIPOTILLR7HU4T/bundle.json","state_url":"https://pith.science/pith/MRPJTVDQIL3CFIPOTILLR7HU4T/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MRPJTVDQIL3CFIPOTILLR7HU4T/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":"2026-06-10T01:26:46Z","links":{"resolver":"https://pith.science/pith/MRPJTVDQIL3CFIPOTILLR7HU4T","bundle":"https://pith.science/pith/MRPJTVDQIL3CFIPOTILLR7HU4T/bundle.json","state":"https://pith.science/pith/MRPJTVDQIL3CFIPOTILLR7HU4T/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MRPJTVDQIL3CFIPOTILLR7HU4T/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:MRPJTVDQIL3CFIPOTILLR7HU4T","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":"0f1e45abd724b196d31aab88eb26bd7d5d0e296fca23e1a11a7d6842ff781034","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-23T08:12:45Z","title_canon_sha256":"2a30c6a9dfe2973a6c0690bbe935823eae4b515cb0fa3de9844dc01cf70a093f"},"schema_version":"1.0","source":{"id":"1710.08135","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.08135","created_at":"2026-05-18T00:32:17Z"},{"alias_kind":"arxiv_version","alias_value":"1710.08135v1","created_at":"2026-05-18T00:32:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.08135","created_at":"2026-05-18T00:32:17Z"},{"alias_kind":"pith_short_12","alias_value":"MRPJTVDQIL3C","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_16","alias_value":"MRPJTVDQIL3CFIPO","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_8","alias_value":"MRPJTVDQ","created_at":"2026-05-18T12:31:31Z"}],"graph_snapshots":[{"event_id":"sha256:9cc0e86bfa08f2aa4313dad30f452497d7ec27dfc079ced870df090ef1fb59c4","target":"graph","created_at":"2026-05-18T00:32:17Z","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":"In the Canadian's lumber industry, simulators are used to predict the lumbers resulting from the sawing of a log at a given sawmill. Giving a log or several logs' 3D scans as input, simulators perform a real-time job to predict the lumbers. These simulators, however, tend to be slow at processing large volume of wood. We thus explore an alternative approximation techniques based on the Iterative Closest Point (ICP) algorithm to identify the already processed log to which an unseen log resembles the most. The main benefit of the ICP approach is that it can easily handle 3D scans with a variable","authors_text":"Cyrine Selma (CRAN), Hind Haouzi (CRAN), Jonathan Gaudreault, Michael Morin, Philippe Thomas (CRAN)","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-23T08:12:45Z","title":"An iterative closest point method for measuring the level of similarity of 3d log scans in wood industry"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.08135","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":"sha256:984bbc8d1c4e9fb1d25b9faadadc76baea67e1f36afb5114eef52c7e8b7272a5","target":"record","created_at":"2026-05-18T00:32:17Z","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":"0f1e45abd724b196d31aab88eb26bd7d5d0e296fca23e1a11a7d6842ff781034","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-23T08:12:45Z","title_canon_sha256":"2a30c6a9dfe2973a6c0690bbe935823eae4b515cb0fa3de9844dc01cf70a093f"},"schema_version":"1.0","source":{"id":"1710.08135","kind":"arxiv","version":1}},"canonical_sha256":"645e99d47042f622a1ee9a16b8fcf4e4c40990f43f2a9a8601e17d5e6199808c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"645e99d47042f622a1ee9a16b8fcf4e4c40990f43f2a9a8601e17d5e6199808c","first_computed_at":"2026-05-18T00:32:17.100639Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:32:17.100639Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"z5JCQqH0iMaba0bZm/nHt+TrjLtz3M+5ke7g/aL3FfhUM87/0/dL+MsKkP2I3LSvwI7raH3ndQJwohf4DbOeDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:32:17.101188Z","signed_message":"canonical_sha256_bytes"},"source_id":"1710.08135","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:984bbc8d1c4e9fb1d25b9faadadc76baea67e1f36afb5114eef52c7e8b7272a5","sha256:9cc0e86bfa08f2aa4313dad30f452497d7ec27dfc079ced870df090ef1fb59c4"],"state_sha256":"795c4670f8cdf9a2c9bfe5497d36846e46d8ab222d5310e1a4f8dd0340fe543e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dCkQMsiaIlA+YKEg+ljGHPxh5CKkFmk5DLxNz0peeHRoBOGLJtyTfjdXXUgVmsiNNlje1Z/rxZbFNbTZVBhYCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-10T01:26:46.533331Z","bundle_sha256":"bc446f1fbf2c4f6a21ac830df1b925a6e02b18835d13cb5c5897c483af901d3e"}}