{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:GBUQE2C24JPHHTBVUKNT76Z7RV","short_pith_number":"pith:GBUQE2C2","canonical_record":{"source":{"id":"1702.08680","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2017-02-28T07:54:21Z","cross_cats_sorted":["cs.MM"],"title_canon_sha256":"74e674cb8e2e14f9493951a58fd3f377c67e6d309b96291f03b2484902938f16","abstract_canon_sha256":"2cf3f1a34c3a6909a9b0eea7d0199f8284b1d219ee43f21931d3c5161fde3af6"},"schema_version":"1.0"},"canonical_sha256":"306902685ae25e73cc35a29b3ffb3f8d54cb406feb46b30d739932248aa96da8","source":{"kind":"arxiv","id":"1702.08680","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1702.08680","created_at":"2026-05-18T00:49:48Z"},{"alias_kind":"arxiv_version","alias_value":"1702.08680v1","created_at":"2026-05-18T00:49:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.08680","created_at":"2026-05-18T00:49:48Z"},{"alias_kind":"pith_short_12","alias_value":"GBUQE2C24JPH","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"GBUQE2C24JPHHTBV","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"GBUQE2C2","created_at":"2026-05-18T12:31:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:GBUQE2C24JPHHTBVUKNT76Z7RV","target":"record","payload":{"canonical_record":{"source":{"id":"1702.08680","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2017-02-28T07:54:21Z","cross_cats_sorted":["cs.MM"],"title_canon_sha256":"74e674cb8e2e14f9493951a58fd3f377c67e6d309b96291f03b2484902938f16","abstract_canon_sha256":"2cf3f1a34c3a6909a9b0eea7d0199f8284b1d219ee43f21931d3c5161fde3af6"},"schema_version":"1.0"},"canonical_sha256":"306902685ae25e73cc35a29b3ffb3f8d54cb406feb46b30d739932248aa96da8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:49:48.123605Z","signature_b64":"ELAMRmB+7oUqGHlXVYSHRRoAL0PQ8BJIF/UKao469a0pK6NfaRXMPP5fEKdpcrNsgdf4n8yji1HA6Tq5lY6hCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"306902685ae25e73cc35a29b3ffb3f8d54cb406feb46b30d739932248aa96da8","last_reissued_at":"2026-05-18T00:49:48.123083Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:49:48.123083Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1702.08680","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":"2026-05-18T00:49:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pt3pH9mP8uq/dTITTNEAUmpC1doNcZr3Fn7TscR9PoD5WF+XCL38SwSOi4V6Q7qX+xFtUUyrsYHiqGkHY3JpAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T07:34:18.803626Z"},"content_sha256":"3d57caa1ac850c4027b6e73721c96aab3b404be8544a990fc03e249815705309","schema_version":"1.0","event_id":"sha256:3d57caa1ac850c4027b6e73721c96aab3b404be8544a990fc03e249815705309"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:GBUQE2C24JPHHTBVUKNT76Z7RV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Data-driven Approach for Furniture and Indoor Scene Colorization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MM"],"primary_cat":"cs.GR","authors_text":"Han Ma, Jie Zhu, Yanwen Guo","submitted_at":"2017-02-28T07:54:21Z","abstract_excerpt":"We present a data-driven approach that colorizes 3D furniture models and indoor scenes by leveraging indoor images on the internet. Our approach is able to colorize the furniture automatically according to an example image. The core is to learn image-guided mesh segmentation to segment the model into different parts according to the image object. Given an indoor scene, the system supports colorization-by-example, and has the ability to recommend the colorization scheme that is consistent with a user-desired color theme. The latter is realized by formulating the problem as a Markov random field"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.08680","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":"2026-05-18T00:49:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4s2p6VwAgc67to+2MG6FSG9sUxHmxEnd288ICtkNI6MZrLRxB3f/29s5tzuJ6qo2CHdsOJnMKcneBNNEInVNDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T07:34:18.803962Z"},"content_sha256":"fcd6e7f8b9baef7382d83541c098659653c7ea9ac8735a87424fe57ba315db98","schema_version":"1.0","event_id":"sha256:fcd6e7f8b9baef7382d83541c098659653c7ea9ac8735a87424fe57ba315db98"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GBUQE2C24JPHHTBVUKNT76Z7RV/bundle.json","state_url":"https://pith.science/pith/GBUQE2C24JPHHTBVUKNT76Z7RV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GBUQE2C24JPHHTBVUKNT76Z7RV/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":"2026-07-03T07:34:18Z","links":{"resolver":"https://pith.science/pith/GBUQE2C24JPHHTBVUKNT76Z7RV","bundle":"https://pith.science/pith/GBUQE2C24JPHHTBVUKNT76Z7RV/bundle.json","state":"https://pith.science/pith/GBUQE2C24JPHHTBVUKNT76Z7RV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GBUQE2C24JPHHTBVUKNT76Z7RV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:GBUQE2C24JPHHTBVUKNT76Z7RV","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":"2cf3f1a34c3a6909a9b0eea7d0199f8284b1d219ee43f21931d3c5161fde3af6","cross_cats_sorted":["cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2017-02-28T07:54:21Z","title_canon_sha256":"74e674cb8e2e14f9493951a58fd3f377c67e6d309b96291f03b2484902938f16"},"schema_version":"1.0","source":{"id":"1702.08680","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1702.08680","created_at":"2026-05-18T00:49:48Z"},{"alias_kind":"arxiv_version","alias_value":"1702.08680v1","created_at":"2026-05-18T00:49:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.08680","created_at":"2026-05-18T00:49:48Z"},{"alias_kind":"pith_short_12","alias_value":"GBUQE2C24JPH","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"GBUQE2C24JPHHTBV","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"GBUQE2C2","created_at":"2026-05-18T12:31:15Z"}],"graph_snapshots":[{"event_id":"sha256:fcd6e7f8b9baef7382d83541c098659653c7ea9ac8735a87424fe57ba315db98","target":"graph","created_at":"2026-05-18T00:49:48Z","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":"We present a data-driven approach that colorizes 3D furniture models and indoor scenes by leveraging indoor images on the internet. Our approach is able to colorize the furniture automatically according to an example image. The core is to learn image-guided mesh segmentation to segment the model into different parts according to the image object. Given an indoor scene, the system supports colorization-by-example, and has the ability to recommend the colorization scheme that is consistent with a user-desired color theme. The latter is realized by formulating the problem as a Markov random field","authors_text":"Han Ma, Jie Zhu, Yanwen Guo","cross_cats":["cs.MM"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2017-02-28T07:54:21Z","title":"A Data-driven Approach for Furniture and Indoor Scene Colorization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.08680","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":"sha256:3d57caa1ac850c4027b6e73721c96aab3b404be8544a990fc03e249815705309","target":"record","created_at":"2026-05-18T00:49:48Z","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":"2cf3f1a34c3a6909a9b0eea7d0199f8284b1d219ee43f21931d3c5161fde3af6","cross_cats_sorted":["cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2017-02-28T07:54:21Z","title_canon_sha256":"74e674cb8e2e14f9493951a58fd3f377c67e6d309b96291f03b2484902938f16"},"schema_version":"1.0","source":{"id":"1702.08680","kind":"arxiv","version":1}},"canonical_sha256":"306902685ae25e73cc35a29b3ffb3f8d54cb406feb46b30d739932248aa96da8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"306902685ae25e73cc35a29b3ffb3f8d54cb406feb46b30d739932248aa96da8","first_computed_at":"2026-05-18T00:49:48.123083Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:49:48.123083Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ELAMRmB+7oUqGHlXVYSHRRoAL0PQ8BJIF/UKao469a0pK6NfaRXMPP5fEKdpcrNsgdf4n8yji1HA6Tq5lY6hCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:49:48.123605Z","signed_message":"canonical_sha256_bytes"},"source_id":"1702.08680","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3d57caa1ac850c4027b6e73721c96aab3b404be8544a990fc03e249815705309","sha256:fcd6e7f8b9baef7382d83541c098659653c7ea9ac8735a87424fe57ba315db98"],"state_sha256":"2092400efed3288099217a51f6393c9e6f21d77d7023d4a0d078ea18cc84aebc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oIvssnV/IkmPrhR9MbtP7IzIPQ/vmoo8v4AkRx9JVmk9GWRZkfuZ27XVEQfQ+c2P+T+KeQx4mjpWyKYjxlYlDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-03T07:34:18.805812Z","bundle_sha256":"99eebee654799fe710a0497999543ba9dd90d176380b4cd38ba28429c23c4a9e"}}