{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:KX46PQCYETVQKLHIJFRNYVQFBG","short_pith_number":"pith:KX46PQCY","schema_version":"1.0","canonical_sha256":"55f9e7c05824eb052ce84962dc56050996e75609eb09affc64ed889f152c1ea9","source":{"kind":"arxiv","id":"2606.00404","version":1},"attestation_state":"computed","paper":{"title":"Rethinking Amortized Neural Representations for High-Resolution Terrain Elevation Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Haoan Feng, Leila De Floriani, Xin Xu","submitted_at":"2026-05-29T22:44:52Z","abstract_excerpt":"Implicit neural representations (INRs) model a signal as a continuous coordinate-to-value function. For terrain elevation data, this supports analytic derivatives, arbitrary-resolution decoding, and a smooth surface model of the underlying heightfield. However, fitting and storing a separate INR for every tile does not scale to large terrain datasets. Amortized neural representations reduce this cost with a shared network: a new tile is mapped to a compact per-tile payload, and a shared decoder reconstructs the heightfield from it. Most such methods are hypernetworks that predict the payload i"},"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":"2606.00404","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-29T22:44:52Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"2121455ff0fcbd14a4babb5cca6c200a637919e113c3b57a38c7da5d9583cd8d","abstract_canon_sha256":"f9244bf6038762f678b4bf9692a59aeb0d6b0d7042dd319618dfac4d699d4569"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T01:03:53.743543Z","signature_b64":"fQDnPchmHJHi7o9EzHodRPJXDalJ70AOjgija9WmVVFrd0nVbhohVozPHk54Id9z6hSP4w7/Vkahnq9he/6hAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"55f9e7c05824eb052ce84962dc56050996e75609eb09affc64ed889f152c1ea9","last_reissued_at":"2026-06-02T01:03:53.742424Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T01:03:53.742424Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Rethinking Amortized Neural Representations for High-Resolution Terrain Elevation Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Haoan Feng, Leila De Floriani, Xin Xu","submitted_at":"2026-05-29T22:44:52Z","abstract_excerpt":"Implicit neural representations (INRs) model a signal as a continuous coordinate-to-value function. For terrain elevation data, this supports analytic derivatives, arbitrary-resolution decoding, and a smooth surface model of the underlying heightfield. However, fitting and storing a separate INR for every tile does not scale to large terrain datasets. Amortized neural representations reduce this cost with a shared network: a new tile is mapped to a compact per-tile payload, and a shared decoder reconstructs the heightfield from it. Most such methods are hypernetworks that predict the payload i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.00404","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/2606.00404/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":"2606.00404","created_at":"2026-06-02T01:03:53.742474+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.00404v1","created_at":"2026-06-02T01:03:53.742474+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.00404","created_at":"2026-06-02T01:03:53.742474+00:00"},{"alias_kind":"pith_short_12","alias_value":"KX46PQCYETVQ","created_at":"2026-06-02T01:03:53.742474+00:00"},{"alias_kind":"pith_short_16","alias_value":"KX46PQCYETVQKLHI","created_at":"2026-06-02T01:03:53.742474+00:00"},{"alias_kind":"pith_short_8","alias_value":"KX46PQCY","created_at":"2026-06-02T01:03:53.742474+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/KX46PQCYETVQKLHIJFRNYVQFBG","json":"https://pith.science/pith/KX46PQCYETVQKLHIJFRNYVQFBG.json","graph_json":"https://pith.science/api/pith-number/KX46PQCYETVQKLHIJFRNYVQFBG/graph.json","events_json":"https://pith.science/api/pith-number/KX46PQCYETVQKLHIJFRNYVQFBG/events.json","paper":"https://pith.science/paper/KX46PQCY"},"agent_actions":{"view_html":"https://pith.science/pith/KX46PQCYETVQKLHIJFRNYVQFBG","download_json":"https://pith.science/pith/KX46PQCYETVQKLHIJFRNYVQFBG.json","view_paper":"https://pith.science/paper/KX46PQCY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.00404&json=true","fetch_graph":"https://pith.science/api/pith-number/KX46PQCYETVQKLHIJFRNYVQFBG/graph.json","fetch_events":"https://pith.science/api/pith-number/KX46PQCYETVQKLHIJFRNYVQFBG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KX46PQCYETVQKLHIJFRNYVQFBG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KX46PQCYETVQKLHIJFRNYVQFBG/action/storage_attestation","attest_author":"https://pith.science/pith/KX46PQCYETVQKLHIJFRNYVQFBG/action/author_attestation","sign_citation":"https://pith.science/pith/KX46PQCYETVQKLHIJFRNYVQFBG/action/citation_signature","submit_replication":"https://pith.science/pith/KX46PQCYETVQKLHIJFRNYVQFBG/action/replication_record"}},"created_at":"2026-06-02T01:03:53.742474+00:00","updated_at":"2026-06-02T01:03:53.742474+00:00"}