{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:OK6MTWY552AF3IWBOTCU3JRRFZ","short_pith_number":"pith:OK6MTWY5","schema_version":"1.0","canonical_sha256":"72bcc9db1dee805da2c174c54da6312e59e7a45165ab344a5e3711d6d1e980cd","source":{"kind":"arxiv","id":"2504.14132","version":1},"attestation_state":"computed","paper":{"title":"HFBRI-MAE: Handcrafted Feature Based Rotation-Invariant Masked Autoencoder for 3D Point Cloud Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dingxin Zhang, Jianhui Yu, Weidong Cai, Xuanhua Yin","submitted_at":"2025-04-19T01:33:19Z","abstract_excerpt":"Self-supervised learning (SSL) has demonstrated remarkable success in 3D point cloud analysis, particularly through masked autoencoders (MAEs). However, existing MAE-based methods lack rotation invariance, leading to significant performance degradation when processing arbitrarily rotated point clouds in real-world scenarios. To address this limitation, we introduce Handcrafted Feature-Based Rotation-Invariant Masked Autoencoder (HFBRI-MAE), a novel framework that refines the MAE design with rotation-invariant handcrafted features to ensure stable feature learning across different orientations."},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2504.14132","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-04-19T01:33:19Z","cross_cats_sorted":[],"title_canon_sha256":"fbf93321c02f068fbae92c5861f36233eeb91a18999f04327d1446396b9a17e9","abstract_canon_sha256":"caa2273c22d66c86ec1c4d0140a82cdcf7831e5c847e7d8a6e7b5e2ec24a4e6e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:51:11.406013Z","signature_b64":"KEqzdVyOcshGY0kBq0Spz/k6bg3s2hFQetqE4y7GGiu0bHdVjupAfK1w9gf4abMFzywpvJz45R7LcvbiPuTRAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"72bcc9db1dee805da2c174c54da6312e59e7a45165ab344a5e3711d6d1e980cd","last_reissued_at":"2026-07-05T10:51:11.405430Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:51:11.405430Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HFBRI-MAE: Handcrafted Feature Based Rotation-Invariant Masked Autoencoder for 3D Point Cloud Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dingxin Zhang, Jianhui Yu, Weidong Cai, Xuanhua Yin","submitted_at":"2025-04-19T01:33:19Z","abstract_excerpt":"Self-supervised learning (SSL) has demonstrated remarkable success in 3D point cloud analysis, particularly through masked autoencoders (MAEs). However, existing MAE-based methods lack rotation invariance, leading to significant performance degradation when processing arbitrarily rotated point clouds in real-world scenarios. To address this limitation, we introduce Handcrafted Feature-Based Rotation-Invariant Masked Autoencoder (HFBRI-MAE), a novel framework that refines the MAE design with rotation-invariant handcrafted features to ensure stable feature learning across different orientations."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.14132","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/2504.14132/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2504.14132","created_at":"2026-07-05T10:51:11.405490+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.14132v1","created_at":"2026-07-05T10:51:11.405490+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.14132","created_at":"2026-07-05T10:51:11.405490+00:00"},{"alias_kind":"pith_short_12","alias_value":"OK6MTWY552AF","created_at":"2026-07-05T10:51:11.405490+00:00"},{"alias_kind":"pith_short_16","alias_value":"OK6MTWY552AF3IWB","created_at":"2026-07-05T10:51:11.405490+00:00"},{"alias_kind":"pith_short_8","alias_value":"OK6MTWY5","created_at":"2026-07-05T10:51:11.405490+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OK6MTWY552AF3IWBOTCU3JRRFZ","json":"https://pith.science/pith/OK6MTWY552AF3IWBOTCU3JRRFZ.json","graph_json":"https://pith.science/api/pith-number/OK6MTWY552AF3IWBOTCU3JRRFZ/graph.json","events_json":"https://pith.science/api/pith-number/OK6MTWY552AF3IWBOTCU3JRRFZ/events.json","paper":"https://pith.science/paper/OK6MTWY5"},"agent_actions":{"view_html":"https://pith.science/pith/OK6MTWY552AF3IWBOTCU3JRRFZ","download_json":"https://pith.science/pith/OK6MTWY552AF3IWBOTCU3JRRFZ.json","view_paper":"https://pith.science/paper/OK6MTWY5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.14132&json=true","fetch_graph":"https://pith.science/api/pith-number/OK6MTWY552AF3IWBOTCU3JRRFZ/graph.json","fetch_events":"https://pith.science/api/pith-number/OK6MTWY552AF3IWBOTCU3JRRFZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OK6MTWY552AF3IWBOTCU3JRRFZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OK6MTWY552AF3IWBOTCU3JRRFZ/action/storage_attestation","attest_author":"https://pith.science/pith/OK6MTWY552AF3IWBOTCU3JRRFZ/action/author_attestation","sign_citation":"https://pith.science/pith/OK6MTWY552AF3IWBOTCU3JRRFZ/action/citation_signature","submit_replication":"https://pith.science/pith/OK6MTWY552AF3IWBOTCU3JRRFZ/action/replication_record"}},"created_at":"2026-07-05T10:51:11.405490+00:00","updated_at":"2026-07-05T10:51:11.405490+00:00"}