{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:JM5PMEW4IPTNNQG5YB5F562UJX","short_pith_number":"pith:JM5PMEW4","canonical_record":{"source":{"id":"1705.11050","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2017-05-31T12:10:32Z","cross_cats_sorted":[],"title_canon_sha256":"c533a0dde3b70b30993a51a18ca50386e6ee6513d4bf4ffd76607ab51a7954da","abstract_canon_sha256":"f3cdce175b120751bbbfee4ed81c5eab3039c60921760fe48ec684ae02f3a520"},"schema_version":"1.0"},"canonical_sha256":"4b3af612dc43e6d6c0ddc07a5efb544df64e28ce738a33c5e128fbb9aa43b379","source":{"kind":"arxiv","id":"1705.11050","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.11050","created_at":"2026-05-18T00:24:04Z"},{"alias_kind":"arxiv_version","alias_value":"1705.11050v2","created_at":"2026-05-18T00:24:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.11050","created_at":"2026-05-18T00:24:04Z"},{"alias_kind":"pith_short_12","alias_value":"JM5PMEW4IPTN","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_16","alias_value":"JM5PMEW4IPTNNQG5","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_8","alias_value":"JM5PMEW4","created_at":"2026-05-18T12:31:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:JM5PMEW4IPTNNQG5YB5F562UJX","target":"record","payload":{"canonical_record":{"source":{"id":"1705.11050","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2017-05-31T12:10:32Z","cross_cats_sorted":[],"title_canon_sha256":"c533a0dde3b70b30993a51a18ca50386e6ee6513d4bf4ffd76607ab51a7954da","abstract_canon_sha256":"f3cdce175b120751bbbfee4ed81c5eab3039c60921760fe48ec684ae02f3a520"},"schema_version":"1.0"},"canonical_sha256":"4b3af612dc43e6d6c0ddc07a5efb544df64e28ce738a33c5e128fbb9aa43b379","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:24:04.449903Z","signature_b64":"Sd1G4gRdxC+VXAEsJMeqO7kdw87n6PuRnW0lc8ItLOb4QYe/gsdvrz/fe9S5SxQkroEgpIB3wlZ+GXatM551Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4b3af612dc43e6d6c0ddc07a5efb544df64e28ce738a33c5e128fbb9aa43b379","last_reissued_at":"2026-05-18T00:24:04.420527Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:24:04.420527Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1705.11050","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:24:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sjQW7wpD1fGQGSikdjYYHAayDk1reFOV0ZX2jxtzbbPYopzbyTs6KbrqQ8Xqr/2ZkfWaYPhpOT9LRu1UhVXuDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T05:07:14.168068Z"},"content_sha256":"a7ffae8b72de783c1a4f0ba20e28d68c68a1dd9b22f41fa55fd4a663dabcbeca","schema_version":"1.0","event_id":"sha256:a7ffae8b72de783c1a4f0ba20e28d68c68a1dd9b22f41fa55fd4a663dabcbeca"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:JM5PMEW4IPTNNQG5YB5F562UJX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"3D Mesh Segmentation via Multi-branch 1D Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.GR","authors_text":"David George, Gary KL Tam, Xianghua Xie","submitted_at":"2017-05-31T12:10:32Z","abstract_excerpt":"There is an increasing interest in applying deep learning to 3D mesh segmentation. We observe that 1) existing feature-based techniques are often slow or sensitive to feature resizing, 2) there are minimal comparative studies and 3) techniques often suffer from reproducibility issue. This study contributes in two ways. First, we propose a novel convolutional neural network (CNN) for mesh segmentation. It uses 1D data, filters and a multi-branch architecture for separate training of multi-scale features. Together with a novel way of computing conformal factor (CF), our technique clearly out-per"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.11050","kind":"arxiv","version":2},"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":"2026-05-18T00:24:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GThLDfTS5NueT555VI/hSbtTdamahI4HdmniTVg3s83sk5EVpG5AVNZzfhDGzkNxK4v+an6MzCr0QpAcxhKrCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T05:07:14.168739Z"},"content_sha256":"0b9980b664b3f52da75f076b39ecebce1596625b9a8969864939842ca8ab181d","schema_version":"1.0","event_id":"sha256:0b9980b664b3f52da75f076b39ecebce1596625b9a8969864939842ca8ab181d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JM5PMEW4IPTNNQG5YB5F562UJX/bundle.json","state_url":"https://pith.science/pith/JM5PMEW4IPTNNQG5YB5F562UJX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JM5PMEW4IPTNNQG5YB5F562UJX/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":"2026-05-26T05:07:14Z","links":{"resolver":"https://pith.science/pith/JM5PMEW4IPTNNQG5YB5F562UJX","bundle":"https://pith.science/pith/JM5PMEW4IPTNNQG5YB5F562UJX/bundle.json","state":"https://pith.science/pith/JM5PMEW4IPTNNQG5YB5F562UJX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JM5PMEW4IPTNNQG5YB5F562UJX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:JM5PMEW4IPTNNQG5YB5F562UJX","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":"f3cdce175b120751bbbfee4ed81c5eab3039c60921760fe48ec684ae02f3a520","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2017-05-31T12:10:32Z","title_canon_sha256":"c533a0dde3b70b30993a51a18ca50386e6ee6513d4bf4ffd76607ab51a7954da"},"schema_version":"1.0","source":{"id":"1705.11050","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.11050","created_at":"2026-05-18T00:24:04Z"},{"alias_kind":"arxiv_version","alias_value":"1705.11050v2","created_at":"2026-05-18T00:24:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.11050","created_at":"2026-05-18T00:24:04Z"},{"alias_kind":"pith_short_12","alias_value":"JM5PMEW4IPTN","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_16","alias_value":"JM5PMEW4IPTNNQG5","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_8","alias_value":"JM5PMEW4","created_at":"2026-05-18T12:31:24Z"}],"graph_snapshots":[{"event_id":"sha256:0b9980b664b3f52da75f076b39ecebce1596625b9a8969864939842ca8ab181d","target":"graph","created_at":"2026-05-18T00:24:04Z","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":"There is an increasing interest in applying deep learning to 3D mesh segmentation. We observe that 1) existing feature-based techniques are often slow or sensitive to feature resizing, 2) there are minimal comparative studies and 3) techniques often suffer from reproducibility issue. This study contributes in two ways. First, we propose a novel convolutional neural network (CNN) for mesh segmentation. It uses 1D data, filters and a multi-branch architecture for separate training of multi-scale features. Together with a novel way of computing conformal factor (CF), our technique clearly out-per","authors_text":"David George, Gary KL Tam, Xianghua Xie","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2017-05-31T12:10:32Z","title":"3D Mesh Segmentation via Multi-branch 1D Convolutional Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.11050","kind":"arxiv","version":2},"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:a7ffae8b72de783c1a4f0ba20e28d68c68a1dd9b22f41fa55fd4a663dabcbeca","target":"record","created_at":"2026-05-18T00:24:04Z","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":"f3cdce175b120751bbbfee4ed81c5eab3039c60921760fe48ec684ae02f3a520","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2017-05-31T12:10:32Z","title_canon_sha256":"c533a0dde3b70b30993a51a18ca50386e6ee6513d4bf4ffd76607ab51a7954da"},"schema_version":"1.0","source":{"id":"1705.11050","kind":"arxiv","version":2}},"canonical_sha256":"4b3af612dc43e6d6c0ddc07a5efb544df64e28ce738a33c5e128fbb9aa43b379","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4b3af612dc43e6d6c0ddc07a5efb544df64e28ce738a33c5e128fbb9aa43b379","first_computed_at":"2026-05-18T00:24:04.420527Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:24:04.420527Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Sd1G4gRdxC+VXAEsJMeqO7kdw87n6PuRnW0lc8ItLOb4QYe/gsdvrz/fe9S5SxQkroEgpIB3wlZ+GXatM551Aw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:24:04.449903Z","signed_message":"canonical_sha256_bytes"},"source_id":"1705.11050","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a7ffae8b72de783c1a4f0ba20e28d68c68a1dd9b22f41fa55fd4a663dabcbeca","sha256:0b9980b664b3f52da75f076b39ecebce1596625b9a8969864939842ca8ab181d"],"state_sha256":"2cb0c5747a07d6166d2c44265f14c59b1ce1ce0ae940bf61672d294cb4b6b6dd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EhDG5+WKcGNByt2EStXoebZRccqFD9zT6PsmaBlA+4fTuuyphdkOZFoBxnitDCUSwD7OSeh3fvFHWXnp7m+yBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T05:07:14.172267Z","bundle_sha256":"070b6eba993e2898af236045301e5bab9c0477b26bf545bac3ca8d01055df3cb"}}