{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:LG6KUORKCHAJYNA2FYLEF3LBWW","short_pith_number":"pith:LG6KUORK","schema_version":"1.0","canonical_sha256":"59bcaa3a2a11c09c341a2e1642ed61b5aa7f61049f156d98ad097b7c9634f8a6","source":{"kind":"arxiv","id":"1904.00993","version":2},"attestation_state":"computed","paper":{"title":"Equivariant Multi-View Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Carlos Esteves, Christine Allen-Blanchette, Kostas Daniilidis, Yinshuang Xu","submitted_at":"2019-04-01T17:58:17Z","abstract_excerpt":"Several popular approaches to 3D vision tasks process multiple views of the input independently with deep neural networks pre-trained on natural images, achieving view permutation invariance through a single round of pooling over all views. We argue that this operation discards important information and leads to subpar global descriptors. In this paper, we propose a group convolutional approach to multiple view aggregation where convolutions are performed over a discrete subgroup of the rotation group, enabling, thus, joint reasoning over all views in an equivariant (instead of invariant) fash"},"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":"1904.00993","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-04-01T17:58:17Z","cross_cats_sorted":[],"title_canon_sha256":"06c0541b2f96c597a6de040570ab106c883a942e4f356fe702d19bac1931a406","abstract_canon_sha256":"12248c648d72869cfb0fe2a96e5c5ff182d4a6e41a343030182d53330f3c5b79"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:15:04.021515Z","signature_b64":"nSDKkz8iY27hXgp6YrcEi/VA3MDSfvqEsVbpPMBp3ilCH6ma/Jf6jMui+/SLNcoUqhDo8di67zpZwQ4rDRDxDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"59bcaa3a2a11c09c341a2e1642ed61b5aa7f61049f156d98ad097b7c9634f8a6","last_reissued_at":"2026-07-05T00:15:04.021068Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:15:04.021068Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Equivariant Multi-View Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Carlos Esteves, Christine Allen-Blanchette, Kostas Daniilidis, Yinshuang Xu","submitted_at":"2019-04-01T17:58:17Z","abstract_excerpt":"Several popular approaches to 3D vision tasks process multiple views of the input independently with deep neural networks pre-trained on natural images, achieving view permutation invariance through a single round of pooling over all views. We argue that this operation discards important information and leads to subpar global descriptors. In this paper, we propose a group convolutional approach to multiple view aggregation where convolutions are performed over a discrete subgroup of the rotation group, enabling, thus, joint reasoning over all views in an equivariant (instead of invariant) fash"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.00993","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/1904.00993/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":"1904.00993","created_at":"2026-07-05T00:15:04.021120+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.00993v2","created_at":"2026-07-05T00:15:04.021120+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.00993","created_at":"2026-07-05T00:15:04.021120+00:00"},{"alias_kind":"pith_short_12","alias_value":"LG6KUORKCHAJ","created_at":"2026-07-05T00:15:04.021120+00:00"},{"alias_kind":"pith_short_16","alias_value":"LG6KUORKCHAJYNA2","created_at":"2026-07-05T00:15:04.021120+00:00"},{"alias_kind":"pith_short_8","alias_value":"LG6KUORK","created_at":"2026-07-05T00:15:04.021120+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.06479","citing_title":"Risk-Controlled Post-Processing of Decision Policies","ref_index":295,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LG6KUORKCHAJYNA2FYLEF3LBWW","json":"https://pith.science/pith/LG6KUORKCHAJYNA2FYLEF3LBWW.json","graph_json":"https://pith.science/api/pith-number/LG6KUORKCHAJYNA2FYLEF3LBWW/graph.json","events_json":"https://pith.science/api/pith-number/LG6KUORKCHAJYNA2FYLEF3LBWW/events.json","paper":"https://pith.science/paper/LG6KUORK"},"agent_actions":{"view_html":"https://pith.science/pith/LG6KUORKCHAJYNA2FYLEF3LBWW","download_json":"https://pith.science/pith/LG6KUORKCHAJYNA2FYLEF3LBWW.json","view_paper":"https://pith.science/paper/LG6KUORK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.00993&json=true","fetch_graph":"https://pith.science/api/pith-number/LG6KUORKCHAJYNA2FYLEF3LBWW/graph.json","fetch_events":"https://pith.science/api/pith-number/LG6KUORKCHAJYNA2FYLEF3LBWW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LG6KUORKCHAJYNA2FYLEF3LBWW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LG6KUORKCHAJYNA2FYLEF3LBWW/action/storage_attestation","attest_author":"https://pith.science/pith/LG6KUORKCHAJYNA2FYLEF3LBWW/action/author_attestation","sign_citation":"https://pith.science/pith/LG6KUORKCHAJYNA2FYLEF3LBWW/action/citation_signature","submit_replication":"https://pith.science/pith/LG6KUORKCHAJYNA2FYLEF3LBWW/action/replication_record"}},"created_at":"2026-07-05T00:15:04.021120+00:00","updated_at":"2026-07-05T00:15:04.021120+00:00"}