{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:W2K2BW353VGRIQNCQ3PXN35ID4","short_pith_number":"pith:W2K2BW35","canonical_record":{"source":{"id":"1711.05919","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-16T04:22:49Z","cross_cats_sorted":[],"title_canon_sha256":"96f5266fe0456ef76b759f7550a27d018cd35f62cc2d08266c9b8ab5dc925f1c","abstract_canon_sha256":"48ba39c57c13ffbe9514c1335f67dd25cb8a907bdfc527bcbc8f78ca73bef785"},"schema_version":"1.0"},"canonical_sha256":"b695a0db7ddd4d1441a286df76efa81f01d12eb12c5e3a645538c6addb5e5acb","source":{"kind":"arxiv","id":"1711.05919","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.05919","created_at":"2026-05-18T00:00:22Z"},{"alias_kind":"arxiv_version","alias_value":"1711.05919v2","created_at":"2026-05-18T00:00:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.05919","created_at":"2026-05-18T00:00:22Z"},{"alias_kind":"pith_short_12","alias_value":"W2K2BW353VGR","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"W2K2BW353VGRIQNC","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"W2K2BW35","created_at":"2026-05-18T12:31:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:W2K2BW353VGRIQNCQ3PXN35ID4","target":"record","payload":{"canonical_record":{"source":{"id":"1711.05919","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-16T04:22:49Z","cross_cats_sorted":[],"title_canon_sha256":"96f5266fe0456ef76b759f7550a27d018cd35f62cc2d08266c9b8ab5dc925f1c","abstract_canon_sha256":"48ba39c57c13ffbe9514c1335f67dd25cb8a907bdfc527bcbc8f78ca73bef785"},"schema_version":"1.0"},"canonical_sha256":"b695a0db7ddd4d1441a286df76efa81f01d12eb12c5e3a645538c6addb5e5acb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:22.310330Z","signature_b64":"lrDtbVSaR7DCg2gcGCUUpralzTl6gxzPTx/Ga++PrWs7b3Yo0nQNCUJ24xhpaLRUPftkmuzKrYpXtMuTzzuCCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b695a0db7ddd4d1441a286df76efa81f01d12eb12c5e3a645538c6addb5e5acb","last_reissued_at":"2026-05-18T00:00:22.309674Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:22.309674Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1711.05919","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:00:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d9YTNKws5PxEJmVpzpq12tsFLQPNkZY9WFN/OgacITmOJWUhdRLkiTyVckW3aBHPElz6sMj4qP9hECmcSLoGAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T16:04:07.443331Z"},"content_sha256":"522e806a28c8b89dc6eaa88d7294f3e70e7b87bcb721d360732ddfe07c68a63c","schema_version":"1.0","event_id":"sha256:522e806a28c8b89dc6eaa88d7294f3e70e7b87bcb721d360732ddfe07c68a63c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:W2K2BW353VGRIQNCQ3PXN35ID4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Deeply Supervised Good Features to Match for Dense Monocular Reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chamara Saroj Weerasekera, Ian Reid, Ravi Garg, Yasir Latif","submitted_at":"2017-11-16T04:22:49Z","abstract_excerpt":"Visual SLAM (Simultaneous Localization and Mapping) methods typically rely on handcrafted visual features or raw RGB values for establishing correspondences between images. These features, while suitable for sparse mapping, often lead to ambiguous matches in texture-less regions when performing dense reconstruction due to the aperture problem. In this work, we explore the use of learned features for the matching task in dense monocular reconstruction. We propose a novel convolutional neural network (CNN) architecture along with a deeply supervised feature learning scheme for pixel-wise regress"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.05919","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:00:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KFg8YSSrOnxLvZ+GyVgzsuH6SD2EV3v0KEOpG+LAmxgnQR3xu624O6ATcMcrCbK7t9fwV4f5YEe7pRniCADSBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T16:04:07.443996Z"},"content_sha256":"c9e758ab62fe39adf1f3cbccde43506c4ad458afb1497090d44a668f2ca134f5","schema_version":"1.0","event_id":"sha256:c9e758ab62fe39adf1f3cbccde43506c4ad458afb1497090d44a668f2ca134f5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/W2K2BW353VGRIQNCQ3PXN35ID4/bundle.json","state_url":"https://pith.science/pith/W2K2BW353VGRIQNCQ3PXN35ID4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/W2K2BW353VGRIQNCQ3PXN35ID4/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-31T16:04:07Z","links":{"resolver":"https://pith.science/pith/W2K2BW353VGRIQNCQ3PXN35ID4","bundle":"https://pith.science/pith/W2K2BW353VGRIQNCQ3PXN35ID4/bundle.json","state":"https://pith.science/pith/W2K2BW353VGRIQNCQ3PXN35ID4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/W2K2BW353VGRIQNCQ3PXN35ID4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:W2K2BW353VGRIQNCQ3PXN35ID4","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"48ba39c57c13ffbe9514c1335f67dd25cb8a907bdfc527bcbc8f78ca73bef785","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-16T04:22:49Z","title_canon_sha256":"96f5266fe0456ef76b759f7550a27d018cd35f62cc2d08266c9b8ab5dc925f1c"},"schema_version":"1.0","source":{"id":"1711.05919","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.05919","created_at":"2026-05-18T00:00:22Z"},{"alias_kind":"arxiv_version","alias_value":"1711.05919v2","created_at":"2026-05-18T00:00:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.05919","created_at":"2026-05-18T00:00:22Z"},{"alias_kind":"pith_short_12","alias_value":"W2K2BW353VGR","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"W2K2BW353VGRIQNC","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"W2K2BW35","created_at":"2026-05-18T12:31:49Z"}],"graph_snapshots":[{"event_id":"sha256:c9e758ab62fe39adf1f3cbccde43506c4ad458afb1497090d44a668f2ca134f5","target":"graph","created_at":"2026-05-18T00:00:22Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Visual SLAM (Simultaneous Localization and Mapping) methods typically rely on handcrafted visual features or raw RGB values for establishing correspondences between images. These features, while suitable for sparse mapping, often lead to ambiguous matches in texture-less regions when performing dense reconstruction due to the aperture problem. In this work, we explore the use of learned features for the matching task in dense monocular reconstruction. We propose a novel convolutional neural network (CNN) architecture along with a deeply supervised feature learning scheme for pixel-wise regress","authors_text":"Chamara Saroj Weerasekera, Ian Reid, Ravi Garg, Yasir Latif","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-16T04:22:49Z","title":"Learning Deeply Supervised Good Features to Match for Dense Monocular Reconstruction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.05919","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:522e806a28c8b89dc6eaa88d7294f3e70e7b87bcb721d360732ddfe07c68a63c","target":"record","created_at":"2026-05-18T00:00:22Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"48ba39c57c13ffbe9514c1335f67dd25cb8a907bdfc527bcbc8f78ca73bef785","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-16T04:22:49Z","title_canon_sha256":"96f5266fe0456ef76b759f7550a27d018cd35f62cc2d08266c9b8ab5dc925f1c"},"schema_version":"1.0","source":{"id":"1711.05919","kind":"arxiv","version":2}},"canonical_sha256":"b695a0db7ddd4d1441a286df76efa81f01d12eb12c5e3a645538c6addb5e5acb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b695a0db7ddd4d1441a286df76efa81f01d12eb12c5e3a645538c6addb5e5acb","first_computed_at":"2026-05-18T00:00:22.309674Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:00:22.309674Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lrDtbVSaR7DCg2gcGCUUpralzTl6gxzPTx/Ga++PrWs7b3Yo0nQNCUJ24xhpaLRUPftkmuzKrYpXtMuTzzuCCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:00:22.310330Z","signed_message":"canonical_sha256_bytes"},"source_id":"1711.05919","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:522e806a28c8b89dc6eaa88d7294f3e70e7b87bcb721d360732ddfe07c68a63c","sha256:c9e758ab62fe39adf1f3cbccde43506c4ad458afb1497090d44a668f2ca134f5"],"state_sha256":"3e067d16f8af575cd1de7368f04e6dcc5c14b80425ce16bb2736259f12533337"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"voi+XdwCOXjAYCICR10pylIoumUo4eViDR9v+6kFAFO8LuQI01jsC55mWvnrnmcTKGLpFPfbaW5QR6n+nxx/Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T16:04:07.447396Z","bundle_sha256":"3416aaaed8ba63a01e6e49d08b8e0558de105ef417285ffc5b28cb9c1a27c91a"}}