{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:HU22R435ASPHBS4MJ3Q55RE3YB","short_pith_number":"pith:HU22R435","schema_version":"1.0","canonical_sha256":"3d35a8f37d049e70cb8c4ee1dec49bc043bbd8efd2f525727e1c75a347ceadb4","source":{"kind":"arxiv","id":"1708.00919","version":3},"attestation_state":"computed","paper":{"title":"Learning Spherical Convolution for Fast Features from 360{\\deg} Imagery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kristen Grauman, Yu-Chuan Su","submitted_at":"2017-08-02T20:18:10Z","abstract_excerpt":"While 360{\\deg} cameras offer tremendous new possibilities in vision, graphics, and augmented reality, the spherical images they produce make core feature extraction non-trivial. Convolutional neural networks (CNNs) trained on images from perspective cameras yield \"flat\" filters, yet 360{\\deg} images cannot be projected to a single plane without significant distortion. A naive solution that repeatedly projects the viewing sphere to all tangent planes is accurate, but much too computationally intensive for real problems. We propose to learn a spherical convolutional network that translates a pl"},"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":"1708.00919","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-02T20:18:10Z","cross_cats_sorted":[],"title_canon_sha256":"1231a04aff64722a4b759ce1cf4892977d35e9516d378c2633fc541c5970654b","abstract_canon_sha256":"dd4bdded7ff5049d776db3382d84228ac79d6367ca743c583e402b70c3835482"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:58:54.699002Z","signature_b64":"C6z/VNacueGR1cydKn+MaVD/lpacbDrkzo0PEICOrnBp06hcQIXhSrnrSL0nxDUu0WrzUqHo3ksCLPJiOnuWAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3d35a8f37d049e70cb8c4ee1dec49bc043bbd8efd2f525727e1c75a347ceadb4","last_reissued_at":"2026-05-17T23:58:54.698613Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:58:54.698613Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Spherical Convolution for Fast Features from 360{\\deg} Imagery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kristen Grauman, Yu-Chuan Su","submitted_at":"2017-08-02T20:18:10Z","abstract_excerpt":"While 360{\\deg} cameras offer tremendous new possibilities in vision, graphics, and augmented reality, the spherical images they produce make core feature extraction non-trivial. Convolutional neural networks (CNNs) trained on images from perspective cameras yield \"flat\" filters, yet 360{\\deg} images cannot be projected to a single plane without significant distortion. A naive solution that repeatedly projects the viewing sphere to all tangent planes is accurate, but much too computationally intensive for real problems. We propose to learn a spherical convolutional network that translates a pl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.00919","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":""},"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":"1708.00919","created_at":"2026-05-17T23:58:54.698671+00:00"},{"alias_kind":"arxiv_version","alias_value":"1708.00919v3","created_at":"2026-05-17T23:58:54.698671+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.00919","created_at":"2026-05-17T23:58:54.698671+00:00"},{"alias_kind":"pith_short_12","alias_value":"HU22R435ASPH","created_at":"2026-05-18T12:31:18.294218+00:00"},{"alias_kind":"pith_short_16","alias_value":"HU22R435ASPHBS4M","created_at":"2026-05-18T12:31:18.294218+00:00"},{"alias_kind":"pith_short_8","alias_value":"HU22R435","created_at":"2026-05-18T12:31:18.294218+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/HU22R435ASPHBS4MJ3Q55RE3YB","json":"https://pith.science/pith/HU22R435ASPHBS4MJ3Q55RE3YB.json","graph_json":"https://pith.science/api/pith-number/HU22R435ASPHBS4MJ3Q55RE3YB/graph.json","events_json":"https://pith.science/api/pith-number/HU22R435ASPHBS4MJ3Q55RE3YB/events.json","paper":"https://pith.science/paper/HU22R435"},"agent_actions":{"view_html":"https://pith.science/pith/HU22R435ASPHBS4MJ3Q55RE3YB","download_json":"https://pith.science/pith/HU22R435ASPHBS4MJ3Q55RE3YB.json","view_paper":"https://pith.science/paper/HU22R435","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1708.00919&json=true","fetch_graph":"https://pith.science/api/pith-number/HU22R435ASPHBS4MJ3Q55RE3YB/graph.json","fetch_events":"https://pith.science/api/pith-number/HU22R435ASPHBS4MJ3Q55RE3YB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HU22R435ASPHBS4MJ3Q55RE3YB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HU22R435ASPHBS4MJ3Q55RE3YB/action/storage_attestation","attest_author":"https://pith.science/pith/HU22R435ASPHBS4MJ3Q55RE3YB/action/author_attestation","sign_citation":"https://pith.science/pith/HU22R435ASPHBS4MJ3Q55RE3YB/action/citation_signature","submit_replication":"https://pith.science/pith/HU22R435ASPHBS4MJ3Q55RE3YB/action/replication_record"}},"created_at":"2026-05-17T23:58:54.698671+00:00","updated_at":"2026-05-17T23:58:54.698671+00:00"}