{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:XGHCODE647XSPZGMESPOU5SDJ6","short_pith_number":"pith:XGHCODE6","canonical_record":{"source":{"id":"2203.15224","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-03-29T04:16:40Z","cross_cats_sorted":[],"title_canon_sha256":"6b0316abfb95785e68a86f84d5b2e82fdde9b73c5cc3579b3bfde319985606cd","abstract_canon_sha256":"043ee7120d8a3b616734ce88c8ce5e3dab0906c714516bbd3b0b579c3c9b7e14"},"schema_version":"1.0"},"canonical_sha256":"b98e270c9ee7ef27e4cc249eea76434f8aa77cb1b14ab5e01a65a233c59cf6a3","source":{"kind":"arxiv","id":"2203.15224","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.15224","created_at":"2026-07-05T04:55:51Z"},{"alias_kind":"arxiv_version","alias_value":"2203.15224v2","created_at":"2026-07-05T04:55:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.15224","created_at":"2026-07-05T04:55:51Z"},{"alias_kind":"pith_short_12","alias_value":"XGHCODE647XS","created_at":"2026-07-05T04:55:51Z"},{"alias_kind":"pith_short_16","alias_value":"XGHCODE647XSPZGM","created_at":"2026-07-05T04:55:51Z"},{"alias_kind":"pith_short_8","alias_value":"XGHCODE6","created_at":"2026-07-05T04:55:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:XGHCODE647XSPZGMESPOU5SDJ6","target":"record","payload":{"canonical_record":{"source":{"id":"2203.15224","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-03-29T04:16:40Z","cross_cats_sorted":[],"title_canon_sha256":"6b0316abfb95785e68a86f84d5b2e82fdde9b73c5cc3579b3bfde319985606cd","abstract_canon_sha256":"043ee7120d8a3b616734ce88c8ce5e3dab0906c714516bbd3b0b579c3c9b7e14"},"schema_version":"1.0"},"canonical_sha256":"b98e270c9ee7ef27e4cc249eea76434f8aa77cb1b14ab5e01a65a233c59cf6a3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:55:51.484246Z","signature_b64":"WJyn6YvH7fndHBCQdyD6i9I/RLqrDg6iNO4LP7XDq1ih35HNPxQhonAh7wsWR3J88aABOF8eXsW+RmHRgjfRAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b98e270c9ee7ef27e4cc249eea76434f8aa77cb1b14ab5e01a65a233c59cf6a3","last_reissued_at":"2026-07-05T04:55:51.483781Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:55:51.483781Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2203.15224","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":"2026-07-05T04:55:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ds3uZpKrOguCX+8yipGP3J6LQ0b8N61cwcovelWmgwAMafsvQF4unp5SQRuBuOHtJeezXqudGi3UTMqobBReBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T11:02:39.086510Z"},"content_sha256":"0bc7287585b20da67a59881d4cb20452770b6a5efebc8638af35762cad802fda","schema_version":"1.0","event_id":"sha256:0bc7287585b20da67a59881d4cb20452770b6a5efebc8638af35762cad802fda"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:XGHCODE647XSPZGMESPOU5SDJ6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andreas Geiger, Lanyun Zhu, Shangzhan Zhang, Tianrun Chen, Xiao Fu, Xiaowei Zhou, Yichong Lu, Yiyi Liao","submitted_at":"2022-03-29T04:16:40Z","abstract_excerpt":"Large-scale training data with high-quality annotations is critical for training semantic and instance segmentation models. Unfortunately, pixel-wise annotation is labor-intensive and costly, raising the demand for more efficient labeling strategies. In this work, we present a novel 3D-to-2D label transfer method, Panoptic NeRF, which aims for obtaining per-pixel 2D semantic and instance labels from easy-to-obtain coarse 3D bounding primitives. Our method utilizes NeRF as a differentiable tool to unify coarse 3D annotations and 2D semantic cues transferred from existing datasets. We demonstrat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.15224","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2203.15224/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":"2026-07-05T04:55:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fFZTwUsN2vloN1DoKUKMHP4rHv+wo6d7mF1RscNhJjiG2Sda79WDMGB1RtML+BR+YaeUFjTMcjbAVRcTurPUAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T11:02:39.087476Z"},"content_sha256":"f7d01dafd14878d6459cb5f49ebb4a00490421d39156887ff1cb8d86efe93120","schema_version":"1.0","event_id":"sha256:f7d01dafd14878d6459cb5f49ebb4a00490421d39156887ff1cb8d86efe93120"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XGHCODE647XSPZGMESPOU5SDJ6/bundle.json","state_url":"https://pith.science/pith/XGHCODE647XSPZGMESPOU5SDJ6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XGHCODE647XSPZGMESPOU5SDJ6/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":"2026-08-08T11:02:39Z","links":{"resolver":"https://pith.science/pith/XGHCODE647XSPZGMESPOU5SDJ6","bundle":"https://pith.science/pith/XGHCODE647XSPZGMESPOU5SDJ6/bundle.json","state":"https://pith.science/pith/XGHCODE647XSPZGMESPOU5SDJ6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XGHCODE647XSPZGMESPOU5SDJ6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:XGHCODE647XSPZGMESPOU5SDJ6","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":"043ee7120d8a3b616734ce88c8ce5e3dab0906c714516bbd3b0b579c3c9b7e14","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-03-29T04:16:40Z","title_canon_sha256":"6b0316abfb95785e68a86f84d5b2e82fdde9b73c5cc3579b3bfde319985606cd"},"schema_version":"1.0","source":{"id":"2203.15224","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.15224","created_at":"2026-07-05T04:55:51Z"},{"alias_kind":"arxiv_version","alias_value":"2203.15224v2","created_at":"2026-07-05T04:55:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.15224","created_at":"2026-07-05T04:55:51Z"},{"alias_kind":"pith_short_12","alias_value":"XGHCODE647XS","created_at":"2026-07-05T04:55:51Z"},{"alias_kind":"pith_short_16","alias_value":"XGHCODE647XSPZGM","created_at":"2026-07-05T04:55:51Z"},{"alias_kind":"pith_short_8","alias_value":"XGHCODE6","created_at":"2026-07-05T04:55:51Z"}],"graph_snapshots":[{"event_id":"sha256:f7d01dafd14878d6459cb5f49ebb4a00490421d39156887ff1cb8d86efe93120","target":"graph","created_at":"2026-07-05T04:55:51Z","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/2203.15224/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large-scale training data with high-quality annotations is critical for training semantic and instance segmentation models. Unfortunately, pixel-wise annotation is labor-intensive and costly, raising the demand for more efficient labeling strategies. In this work, we present a novel 3D-to-2D label transfer method, Panoptic NeRF, which aims for obtaining per-pixel 2D semantic and instance labels from easy-to-obtain coarse 3D bounding primitives. Our method utilizes NeRF as a differentiable tool to unify coarse 3D annotations and 2D semantic cues transferred from existing datasets. We demonstrat","authors_text":"Andreas Geiger, Lanyun Zhu, Shangzhan Zhang, Tianrun Chen, Xiao Fu, Xiaowei Zhou, Yichong Lu, Yiyi Liao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-03-29T04:16:40Z","title":"Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.15224","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":"sha256:0bc7287585b20da67a59881d4cb20452770b6a5efebc8638af35762cad802fda","target":"record","created_at":"2026-07-05T04:55:51Z","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":"043ee7120d8a3b616734ce88c8ce5e3dab0906c714516bbd3b0b579c3c9b7e14","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-03-29T04:16:40Z","title_canon_sha256":"6b0316abfb95785e68a86f84d5b2e82fdde9b73c5cc3579b3bfde319985606cd"},"schema_version":"1.0","source":{"id":"2203.15224","kind":"arxiv","version":2}},"canonical_sha256":"b98e270c9ee7ef27e4cc249eea76434f8aa77cb1b14ab5e01a65a233c59cf6a3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b98e270c9ee7ef27e4cc249eea76434f8aa77cb1b14ab5e01a65a233c59cf6a3","first_computed_at":"2026-07-05T04:55:51.483781Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:55:51.483781Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WJyn6YvH7fndHBCQdyD6i9I/RLqrDg6iNO4LP7XDq1ih35HNPxQhonAh7wsWR3J88aABOF8eXsW+RmHRgjfRAg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:55:51.484246Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.15224","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0bc7287585b20da67a59881d4cb20452770b6a5efebc8638af35762cad802fda","sha256:f7d01dafd14878d6459cb5f49ebb4a00490421d39156887ff1cb8d86efe93120"],"state_sha256":"9107d52e3aef8729043fc158070d9afc471e9fd97817eaa7fc12319710ee4ac0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t33qQj7TRkcSyA1QNk0prOtSWDclAMI7EQWH1dSYDSS4zzdPdRaBAP2ZF4gZ2dHRpsHhDelVgRckRTINGQ6DAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T11:02:39.094901Z","bundle_sha256":"a4ef08114be28df84ca07e369d9ca171628a3966c77f00ccdd579ed52206500d"}}