{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:EG6SOGE6FHURFOPN254JCAOWSD","short_pith_number":"pith:EG6SOGE6","canonical_record":{"source":{"id":"2104.04687","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-04-10T05:40:42Z","cross_cats_sorted":[],"title_canon_sha256":"b3bbc043e9b46f6fb9479985f0532c3bc3f19df4f788f6c548cda67694036901","abstract_canon_sha256":"ed16fd15456a2893081f51f044cb8707bdccf75feaeb6d8767549395c2913471"},"schema_version":"1.0"},"canonical_sha256":"21bd27189e29e912b9edd7789101d690c6ae594a0d0d636adb7af4a68905bc29","source":{"kind":"arxiv","id":"2104.04687","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2104.04687","created_at":"2026-07-05T03:43:40Z"},{"alias_kind":"arxiv_version","alias_value":"2104.04687v3","created_at":"2026-07-05T03:43:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.04687","created_at":"2026-07-05T03:43:40Z"},{"alias_kind":"pith_short_12","alias_value":"EG6SOGE6FHUR","created_at":"2026-07-05T03:43:40Z"},{"alias_kind":"pith_short_16","alias_value":"EG6SOGE6FHURFOPN","created_at":"2026-07-05T03:43:40Z"},{"alias_kind":"pith_short_8","alias_value":"EG6SOGE6","created_at":"2026-07-05T03:43:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:EG6SOGE6FHURFOPN254JCAOWSD","target":"record","payload":{"canonical_record":{"source":{"id":"2104.04687","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-04-10T05:40:42Z","cross_cats_sorted":[],"title_canon_sha256":"b3bbc043e9b46f6fb9479985f0532c3bc3f19df4f788f6c548cda67694036901","abstract_canon_sha256":"ed16fd15456a2893081f51f044cb8707bdccf75feaeb6d8767549395c2913471"},"schema_version":"1.0"},"canonical_sha256":"21bd27189e29e912b9edd7789101d690c6ae594a0d0d636adb7af4a68905bc29","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:43:40.235972Z","signature_b64":"RWzufCetoUByIsyUb2+6mlomfM6KYJ9tYd/dnVjSQ1t8mt5rZodBHJ2AJNAlg+CKiNpnRugvLyWBBV4SyyxVAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"21bd27189e29e912b9edd7789101d690c6ae594a0d0d636adb7af4a68905bc29","last_reissued_at":"2026-07-05T03:43:40.235493Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:43:40.235493Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2104.04687","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:43:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"J/h6pBAljW9u8W5wImkYMAjiFF7itdipfaoGioPshgLhMxvPhieRlQ/O9xVSvrKWaG81vPY04KWi6vL6gQ2yCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T11:55:22.694627Z"},"content_sha256":"2576110f4c8ff47670fc257b045eefb20abec60c8556d52e58c36d106bafda33","schema_version":"1.0","event_id":"sha256:2576110f4c8ff47670fc257b045eefb20abec60c8556d52e58c36d106bafda33"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:EG6SOGE6FHURFOPN254JCAOWSD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretraining","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ching-Yu Tseng, Chin-Tang Chen, Hung-Ting Su, Hung-Yueh Chiang, Winston H. Hsu, Yueh-Cheng Liu, Yu-Kai Huang, Zhe-Yu Liu","submitted_at":"2021-04-10T05:40:42Z","abstract_excerpt":"Most 3D neural networks are trained from scratch owing to the lack of large-scale labeled 3D datasets. In this paper, we present a novel 3D pretraining method by leveraging 2D networks learned from rich 2D datasets. We propose the contrastive pixel-to-point knowledge transfer to effectively utilize the 2D information by mapping the pixel-level and point-level features into the same embedding space. Due to the heterogeneous nature between 2D and 3D networks, we introduce the back-projection function to align the features between 2D and 3D to make the transfer possible. Additionally, we devise a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.04687","kind":"arxiv","version":3},"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/2104.04687/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":"2026-07-05T03:43:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rvdX+/+MMrby8W9r5S8ZUR/wiHY/G1gecS5lFFYiAioHvXDceYShzLO9+pgYC/Lq3MU93pM1T58SP/zGxjhuBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T11:55:22.695125Z"},"content_sha256":"8f17853cf9118883c62f8b040ecce5110bc688038dd3657dbe59c3bd602e4670","schema_version":"1.0","event_id":"sha256:8f17853cf9118883c62f8b040ecce5110bc688038dd3657dbe59c3bd602e4670"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EG6SOGE6FHURFOPN254JCAOWSD/bundle.json","state_url":"https://pith.science/pith/EG6SOGE6FHURFOPN254JCAOWSD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EG6SOGE6FHURFOPN254JCAOWSD/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":"2026-08-08T11:55:22Z","links":{"resolver":"https://pith.science/pith/EG6SOGE6FHURFOPN254JCAOWSD","bundle":"https://pith.science/pith/EG6SOGE6FHURFOPN254JCAOWSD/bundle.json","state":"https://pith.science/pith/EG6SOGE6FHURFOPN254JCAOWSD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EG6SOGE6FHURFOPN254JCAOWSD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:EG6SOGE6FHURFOPN254JCAOWSD","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":"ed16fd15456a2893081f51f044cb8707bdccf75feaeb6d8767549395c2913471","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-04-10T05:40:42Z","title_canon_sha256":"b3bbc043e9b46f6fb9479985f0532c3bc3f19df4f788f6c548cda67694036901"},"schema_version":"1.0","source":{"id":"2104.04687","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2104.04687","created_at":"2026-07-05T03:43:40Z"},{"alias_kind":"arxiv_version","alias_value":"2104.04687v3","created_at":"2026-07-05T03:43:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.04687","created_at":"2026-07-05T03:43:40Z"},{"alias_kind":"pith_short_12","alias_value":"EG6SOGE6FHUR","created_at":"2026-07-05T03:43:40Z"},{"alias_kind":"pith_short_16","alias_value":"EG6SOGE6FHURFOPN","created_at":"2026-07-05T03:43:40Z"},{"alias_kind":"pith_short_8","alias_value":"EG6SOGE6","created_at":"2026-07-05T03:43:40Z"}],"graph_snapshots":[{"event_id":"sha256:8f17853cf9118883c62f8b040ecce5110bc688038dd3657dbe59c3bd602e4670","target":"graph","created_at":"2026-07-05T03:43:40Z","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/2104.04687/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Most 3D neural networks are trained from scratch owing to the lack of large-scale labeled 3D datasets. In this paper, we present a novel 3D pretraining method by leveraging 2D networks learned from rich 2D datasets. We propose the contrastive pixel-to-point knowledge transfer to effectively utilize the 2D information by mapping the pixel-level and point-level features into the same embedding space. Due to the heterogeneous nature between 2D and 3D networks, we introduce the back-projection function to align the features between 2D and 3D to make the transfer possible. Additionally, we devise a","authors_text":"Ching-Yu Tseng, Chin-Tang Chen, Hung-Ting Su, Hung-Yueh Chiang, Winston H. Hsu, Yueh-Cheng Liu, Yu-Kai Huang, Zhe-Yu Liu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-04-10T05:40:42Z","title":"Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretraining"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.04687","kind":"arxiv","version":3},"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":"sha256:2576110f4c8ff47670fc257b045eefb20abec60c8556d52e58c36d106bafda33","target":"record","created_at":"2026-07-05T03:43:40Z","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":"ed16fd15456a2893081f51f044cb8707bdccf75feaeb6d8767549395c2913471","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-04-10T05:40:42Z","title_canon_sha256":"b3bbc043e9b46f6fb9479985f0532c3bc3f19df4f788f6c548cda67694036901"},"schema_version":"1.0","source":{"id":"2104.04687","kind":"arxiv","version":3}},"canonical_sha256":"21bd27189e29e912b9edd7789101d690c6ae594a0d0d636adb7af4a68905bc29","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"21bd27189e29e912b9edd7789101d690c6ae594a0d0d636adb7af4a68905bc29","first_computed_at":"2026-07-05T03:43:40.235493Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:43:40.235493Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RWzufCetoUByIsyUb2+6mlomfM6KYJ9tYd/dnVjSQ1t8mt5rZodBHJ2AJNAlg+CKiNpnRugvLyWBBV4SyyxVAg==","signature_status":"signed_v1","signed_at":"2026-07-05T03:43:40.235972Z","signed_message":"canonical_sha256_bytes"},"source_id":"2104.04687","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2576110f4c8ff47670fc257b045eefb20abec60c8556d52e58c36d106bafda33","sha256:8f17853cf9118883c62f8b040ecce5110bc688038dd3657dbe59c3bd602e4670"],"state_sha256":"ba401ea4e14149997c8580cf99d68c8b61c6706dfc09dbab87f1c7c67233efdb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EL4ip14wcYYCVzHST+scg+3pL1SZGUV+j/5zTsy4i/xegDRo8CZG26eO+2IVs8qmWJxMh73K2XBP5chd1j0kDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T11:55:22.699607Z","bundle_sha256":"849a28b55e233c18d0afc5b0303aaaf242ea128ef2ddbb4811f02eee51e71f55"}}