{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:M66FSXPO2BC5GBBXNOJD44RY5N","short_pith_number":"pith:M66FSXPO","canonical_record":{"source":{"id":"2408.15637","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-08-28T08:44:58Z","cross_cats_sorted":[],"title_canon_sha256":"34431853c266a19ba5958f5027cdde0924ac931d0daaebefafb54ae2fe717352","abstract_canon_sha256":"b99193d13b7b05622b831d9c53cd47ecfff5ec70a5217d90e175486489310b42"},"schema_version":"1.0"},"canonical_sha256":"67bc595deed045d304376b923e7238eb73f8fe351875af346f78f4fddc73baf9","source":{"kind":"arxiv","id":"2408.15637","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.15637","created_at":"2026-07-05T09:00:14Z"},{"alias_kind":"arxiv_version","alias_value":"2408.15637v1","created_at":"2026-07-05T09:00:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.15637","created_at":"2026-07-05T09:00:14Z"},{"alias_kind":"pith_short_12","alias_value":"M66FSXPO2BC5","created_at":"2026-07-05T09:00:14Z"},{"alias_kind":"pith_short_16","alias_value":"M66FSXPO2BC5GBBX","created_at":"2026-07-05T09:00:14Z"},{"alias_kind":"pith_short_8","alias_value":"M66FSXPO","created_at":"2026-07-05T09:00:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:M66FSXPO2BC5GBBXNOJD44RY5N","target":"record","payload":{"canonical_record":{"source":{"id":"2408.15637","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-08-28T08:44:58Z","cross_cats_sorted":[],"title_canon_sha256":"34431853c266a19ba5958f5027cdde0924ac931d0daaebefafb54ae2fe717352","abstract_canon_sha256":"b99193d13b7b05622b831d9c53cd47ecfff5ec70a5217d90e175486489310b42"},"schema_version":"1.0"},"canonical_sha256":"67bc595deed045d304376b923e7238eb73f8fe351875af346f78f4fddc73baf9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:00:14.908555Z","signature_b64":"MCTSvBo6DnGg68hqtKS3zGCFe/nFHxVTRVqFoBq87WdbipiVTAw9xnqodLNRD7E77Ffge7GOaBhASXZfzwAEAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"67bc595deed045d304376b923e7238eb73f8fe351875af346f78f4fddc73baf9","last_reissued_at":"2026-07-05T09:00:14.908147Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:00:14.908147Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2408.15637","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":"2026-07-05T09:00:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"C3mqjbladbbWvIO0y9f4bPdk4xvlgsWHpvt5ISQD+Vm2tAnoVRZnU/yuU15d7FbIqLhzWGkf56vpi9s43SoEBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T10:04:46.078810Z"},"content_sha256":"daaf7eb69686914721ef3a3d6e3a2bf0c7273b1c223657ece9ca4f45ff608842","schema_version":"1.0","event_id":"sha256:daaf7eb69686914721ef3a3d6e3a2bf0c7273b1c223657ece9ca4f45ff608842"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:M66FSXPO2BC5GBBXNOJD44RY5N","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Transfer Learning from Simulated to Real Scenes for Monocular 3D Object Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ahmed Alaaeldin Ghita, Alois Knoll, Mirko Marras, Modesto Castrill\\'on-Santana, Mohan Trivedi, Ross Greer, Salvatore Mario Carta, Sondos Mohamed, Walter Zimmer","submitted_at":"2024-08-28T08:44:58Z","abstract_excerpt":"Accurately detecting 3D objects from monocular images in dynamic roadside scenarios remains a challenging problem due to varying camera perspectives and unpredictable scene conditions. This paper introduces a two-stage training strategy to address these challenges. Our approach initially trains a model on the large-scale synthetic dataset, RoadSense3D, which offers a diverse range of scenarios for robust feature learning. Subsequently, we fine-tune the model on a combination of real-world datasets to enhance its adaptability to practical conditions. Experimental results of the Cube R-CNN model"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.15637","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/2408.15637/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":"2026-07-05T09:00:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"I7D+XkFDG71ojWLsGQ95bO7HlLlCwUn4UrX2sJNGorNbJkn4sEm+r3jEop4M4bvlkupaILF/T8H/GBxLqsHIBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T10:04:46.079296Z"},"content_sha256":"8d2101453a3c9a251efc23c2fa5054e174a7dae5447ebe5438aeba104be3ec9d","schema_version":"1.0","event_id":"sha256:8d2101453a3c9a251efc23c2fa5054e174a7dae5447ebe5438aeba104be3ec9d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/M66FSXPO2BC5GBBXNOJD44RY5N/bundle.json","state_url":"https://pith.science/pith/M66FSXPO2BC5GBBXNOJD44RY5N/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/M66FSXPO2BC5GBBXNOJD44RY5N/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":"2026-08-04T10:04:46Z","links":{"resolver":"https://pith.science/pith/M66FSXPO2BC5GBBXNOJD44RY5N","bundle":"https://pith.science/pith/M66FSXPO2BC5GBBXNOJD44RY5N/bundle.json","state":"https://pith.science/pith/M66FSXPO2BC5GBBXNOJD44RY5N/state.json","well_known_bundle":"https://pith.science/.well-known/pith/M66FSXPO2BC5GBBXNOJD44RY5N/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:M66FSXPO2BC5GBBXNOJD44RY5N","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":"b99193d13b7b05622b831d9c53cd47ecfff5ec70a5217d90e175486489310b42","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-08-28T08:44:58Z","title_canon_sha256":"34431853c266a19ba5958f5027cdde0924ac931d0daaebefafb54ae2fe717352"},"schema_version":"1.0","source":{"id":"2408.15637","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.15637","created_at":"2026-07-05T09:00:14Z"},{"alias_kind":"arxiv_version","alias_value":"2408.15637v1","created_at":"2026-07-05T09:00:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.15637","created_at":"2026-07-05T09:00:14Z"},{"alias_kind":"pith_short_12","alias_value":"M66FSXPO2BC5","created_at":"2026-07-05T09:00:14Z"},{"alias_kind":"pith_short_16","alias_value":"M66FSXPO2BC5GBBX","created_at":"2026-07-05T09:00:14Z"},{"alias_kind":"pith_short_8","alias_value":"M66FSXPO","created_at":"2026-07-05T09:00:14Z"}],"graph_snapshots":[{"event_id":"sha256:8d2101453a3c9a251efc23c2fa5054e174a7dae5447ebe5438aeba104be3ec9d","target":"graph","created_at":"2026-07-05T09:00:14Z","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/2408.15637/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accurately detecting 3D objects from monocular images in dynamic roadside scenarios remains a challenging problem due to varying camera perspectives and unpredictable scene conditions. This paper introduces a two-stage training strategy to address these challenges. Our approach initially trains a model on the large-scale synthetic dataset, RoadSense3D, which offers a diverse range of scenarios for robust feature learning. Subsequently, we fine-tune the model on a combination of real-world datasets to enhance its adaptability to practical conditions. Experimental results of the Cube R-CNN model","authors_text":"Ahmed Alaaeldin Ghita, Alois Knoll, Mirko Marras, Modesto Castrill\\'on-Santana, Mohan Trivedi, Ross Greer, Salvatore Mario Carta, Sondos Mohamed, Walter Zimmer","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-08-28T08:44:58Z","title":"Transfer Learning from Simulated to Real Scenes for Monocular 3D Object Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.15637","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:daaf7eb69686914721ef3a3d6e3a2bf0c7273b1c223657ece9ca4f45ff608842","target":"record","created_at":"2026-07-05T09:00:14Z","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":"b99193d13b7b05622b831d9c53cd47ecfff5ec70a5217d90e175486489310b42","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-08-28T08:44:58Z","title_canon_sha256":"34431853c266a19ba5958f5027cdde0924ac931d0daaebefafb54ae2fe717352"},"schema_version":"1.0","source":{"id":"2408.15637","kind":"arxiv","version":1}},"canonical_sha256":"67bc595deed045d304376b923e7238eb73f8fe351875af346f78f4fddc73baf9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"67bc595deed045d304376b923e7238eb73f8fe351875af346f78f4fddc73baf9","first_computed_at":"2026-07-05T09:00:14.908147Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:00:14.908147Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MCTSvBo6DnGg68hqtKS3zGCFe/nFHxVTRVqFoBq87WdbipiVTAw9xnqodLNRD7E77Ffge7GOaBhASXZfzwAEAA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:00:14.908555Z","signed_message":"canonical_sha256_bytes"},"source_id":"2408.15637","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:daaf7eb69686914721ef3a3d6e3a2bf0c7273b1c223657ece9ca4f45ff608842","sha256:8d2101453a3c9a251efc23c2fa5054e174a7dae5447ebe5438aeba104be3ec9d"],"state_sha256":"0323df0cbef18988a04ba4869b0bd3e4494d9ff13e4ce7ec08e87984580be0fe"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"o/vziFC6BGpQhzBzHLsyVbOt8ICVG4OwFeF7IPXiyyy8fUrw7RK2Y4laRUN6OxASpuKYQOWt69k04HNfWukyAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T10:04:46.082549Z","bundle_sha256":"4d76e66b670d26251198828521b97bd13b48707f5ad83fab071a4411908c1064"}}