{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:WDNLS7HAGWVOHAV6OVYUK2SXTU","short_pith_number":"pith:WDNLS7HA","schema_version":"1.0","canonical_sha256":"b0dab97ce035aae382be7571456a579d244cfef893bca85d71e52bcb22242435","source":{"kind":"arxiv","id":"2409.08159","version":1},"attestation_state":"computed","paper":{"title":"SDformer: Efficient End-to-End Transformer for Depth Completion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ashley Lee, Jian Qian, Jie Li, Miao Sun, Patrick Yin Chiang, Shenglong Zhuo","submitted_at":"2024-09-12T15:52:08Z","abstract_excerpt":"Depth completion aims to predict dense depth maps with sparse depth measurements from a depth sensor. Currently, Convolutional Neural Network (CNN) based models are the most popular methods applied to depth completion tasks. However, despite the excellent high-end performance, they suffer from a limited representation area. To overcome the drawbacks of CNNs, a more effective and powerful method has been presented: the Transformer, which is an adaptive self-attention setting sequence-to-sequence model. While the standard Transformer quadratically increases the computational cost from the key-qu"},"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":"2409.08159","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-12T15:52:08Z","cross_cats_sorted":[],"title_canon_sha256":"ad0498c917c2514783608f231337de7e633b5404ac2f7b4ece9f6b0b32b62811","abstract_canon_sha256":"082a5379c20d0abfedbaef72a4697c7347b7bd808ee15c55f89295755bef18c6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:06:21.067811Z","signature_b64":"RBOdjyhXpqyvc3prvjFAJhsb2r0bs5wMfx0BKSge9Gz2jcepbgyCUR4OE770tL7Em2wTjl1IUqCIIZJx5cwrBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b0dab97ce035aae382be7571456a579d244cfef893bca85d71e52bcb22242435","last_reissued_at":"2026-07-05T09:06:21.067365Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:06:21.067365Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SDformer: Efficient End-to-End Transformer for Depth Completion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ashley Lee, Jian Qian, Jie Li, Miao Sun, Patrick Yin Chiang, Shenglong Zhuo","submitted_at":"2024-09-12T15:52:08Z","abstract_excerpt":"Depth completion aims to predict dense depth maps with sparse depth measurements from a depth sensor. Currently, Convolutional Neural Network (CNN) based models are the most popular methods applied to depth completion tasks. However, despite the excellent high-end performance, they suffer from a limited representation area. To overcome the drawbacks of CNNs, a more effective and powerful method has been presented: the Transformer, which is an adaptive self-attention setting sequence-to-sequence model. While the standard Transformer quadratically increases the computational cost from the key-qu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.08159","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/2409.08159/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":"2409.08159","created_at":"2026-07-05T09:06:21.067424+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.08159v1","created_at":"2026-07-05T09:06:21.067424+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.08159","created_at":"2026-07-05T09:06:21.067424+00:00"},{"alias_kind":"pith_short_12","alias_value":"WDNLS7HAGWVO","created_at":"2026-07-05T09:06:21.067424+00:00"},{"alias_kind":"pith_short_16","alias_value":"WDNLS7HAGWVOHAV6","created_at":"2026-07-05T09:06:21.067424+00:00"},{"alias_kind":"pith_short_8","alias_value":"WDNLS7HA","created_at":"2026-07-05T09:06:21.067424+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/WDNLS7HAGWVOHAV6OVYUK2SXTU","json":"https://pith.science/pith/WDNLS7HAGWVOHAV6OVYUK2SXTU.json","graph_json":"https://pith.science/api/pith-number/WDNLS7HAGWVOHAV6OVYUK2SXTU/graph.json","events_json":"https://pith.science/api/pith-number/WDNLS7HAGWVOHAV6OVYUK2SXTU/events.json","paper":"https://pith.science/paper/WDNLS7HA"},"agent_actions":{"view_html":"https://pith.science/pith/WDNLS7HAGWVOHAV6OVYUK2SXTU","download_json":"https://pith.science/pith/WDNLS7HAGWVOHAV6OVYUK2SXTU.json","view_paper":"https://pith.science/paper/WDNLS7HA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.08159&json=true","fetch_graph":"https://pith.science/api/pith-number/WDNLS7HAGWVOHAV6OVYUK2SXTU/graph.json","fetch_events":"https://pith.science/api/pith-number/WDNLS7HAGWVOHAV6OVYUK2SXTU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WDNLS7HAGWVOHAV6OVYUK2SXTU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WDNLS7HAGWVOHAV6OVYUK2SXTU/action/storage_attestation","attest_author":"https://pith.science/pith/WDNLS7HAGWVOHAV6OVYUK2SXTU/action/author_attestation","sign_citation":"https://pith.science/pith/WDNLS7HAGWVOHAV6OVYUK2SXTU/action/citation_signature","submit_replication":"https://pith.science/pith/WDNLS7HAGWVOHAV6OVYUK2SXTU/action/replication_record"}},"created_at":"2026-07-05T09:06:21.067424+00:00","updated_at":"2026-07-05T09:06:21.067424+00:00"}