{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:T5NF3K52SBYWQ3ORMQFA5UBILD","short_pith_number":"pith:T5NF3K52","schema_version":"1.0","canonical_sha256":"9f5a5dabba9071686dd1640a0ed02858d2e1f7134cc6c73697f911429f9f4ba9","source":{"kind":"arxiv","id":"1809.09077","version":3},"attestation_state":"computed","paper":{"title":"Incorporating Luminance, Depth and Color Information by a Fusion-based Network for Semantic Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hsueh-Ming Hang, Shang-Wei Hung, Shao-Yuan Lo","submitted_at":"2018-09-24T17:45:35Z","abstract_excerpt":"Semantic segmentation has made encouraging progress due to the success of deep convolutional networks in recent years. Meanwhile, depth sensors become prevalent nowadays, so depth maps can be acquired more easily. However, there are few studies that focus on the RGB-D semantic segmentation task. Exploiting the depth information effectiveness to improve performance is a challenge. In this paper, we propose a novel solution named LDFNet, which incorporates Luminance, Depth and Color information by a fusion-based network. It includes a sub-network to process depth maps and employs luminance image"},"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":"1809.09077","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-24T17:45:35Z","cross_cats_sorted":[],"title_canon_sha256":"e33c44441958e53b72e7785c9c79787f2efb1f14683dc4969aa4e15e20f8024b","abstract_canon_sha256":"778439edb6d5f997ccc6e8006a0c0aeba5dce45b19f587ceb8257099c47b497a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:45:52.812284Z","signature_b64":"iNdkGZ5FZ4zW34hVixrbAt8gYY2OE7m9riW9pCJfcmMFcJvXRlWE0WICJXdkN8suYTxqbYpdubCAiDxmMun5CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9f5a5dabba9071686dd1640a0ed02858d2e1f7134cc6c73697f911429f9f4ba9","last_reissued_at":"2026-05-17T23:45:52.811725Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:45:52.811725Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Incorporating Luminance, Depth and Color Information by a Fusion-based Network for Semantic Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hsueh-Ming Hang, Shang-Wei Hung, Shao-Yuan Lo","submitted_at":"2018-09-24T17:45:35Z","abstract_excerpt":"Semantic segmentation has made encouraging progress due to the success of deep convolutional networks in recent years. Meanwhile, depth sensors become prevalent nowadays, so depth maps can be acquired more easily. However, there are few studies that focus on the RGB-D semantic segmentation task. Exploiting the depth information effectiveness to improve performance is a challenge. In this paper, we propose a novel solution named LDFNet, which incorporates Luminance, Depth and Color information by a fusion-based network. It includes a sub-network to process depth maps and employs luminance image"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.09077","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":"1809.09077","created_at":"2026-05-17T23:45:52.811818+00:00"},{"alias_kind":"arxiv_version","alias_value":"1809.09077v3","created_at":"2026-05-17T23:45:52.811818+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.09077","created_at":"2026-05-17T23:45:52.811818+00:00"},{"alias_kind":"pith_short_12","alias_value":"T5NF3K52SBYW","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_16","alias_value":"T5NF3K52SBYWQ3OR","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_8","alias_value":"T5NF3K52","created_at":"2026-05-18T12:32:53.628368+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/T5NF3K52SBYWQ3ORMQFA5UBILD","json":"https://pith.science/pith/T5NF3K52SBYWQ3ORMQFA5UBILD.json","graph_json":"https://pith.science/api/pith-number/T5NF3K52SBYWQ3ORMQFA5UBILD/graph.json","events_json":"https://pith.science/api/pith-number/T5NF3K52SBYWQ3ORMQFA5UBILD/events.json","paper":"https://pith.science/paper/T5NF3K52"},"agent_actions":{"view_html":"https://pith.science/pith/T5NF3K52SBYWQ3ORMQFA5UBILD","download_json":"https://pith.science/pith/T5NF3K52SBYWQ3ORMQFA5UBILD.json","view_paper":"https://pith.science/paper/T5NF3K52","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1809.09077&json=true","fetch_graph":"https://pith.science/api/pith-number/T5NF3K52SBYWQ3ORMQFA5UBILD/graph.json","fetch_events":"https://pith.science/api/pith-number/T5NF3K52SBYWQ3ORMQFA5UBILD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T5NF3K52SBYWQ3ORMQFA5UBILD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T5NF3K52SBYWQ3ORMQFA5UBILD/action/storage_attestation","attest_author":"https://pith.science/pith/T5NF3K52SBYWQ3ORMQFA5UBILD/action/author_attestation","sign_citation":"https://pith.science/pith/T5NF3K52SBYWQ3ORMQFA5UBILD/action/citation_signature","submit_replication":"https://pith.science/pith/T5NF3K52SBYWQ3ORMQFA5UBILD/action/replication_record"}},"created_at":"2026-05-17T23:45:52.811818+00:00","updated_at":"2026-05-17T23:45:52.811818+00:00"}