{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:LWXCGR7L45CU3CZD3SWOT5AC7L","short_pith_number":"pith:LWXCGR7L","schema_version":"1.0","canonical_sha256":"5dae2347ebe7454d8b23dcace9f402fad8acf6689d6bfa6670a591ad10fe48fe","source":{"kind":"arxiv","id":"2408.00771","version":1},"attestation_state":"computed","paper":{"title":"2D Neural Fields with Learned Discontinuities","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Alec Jacobson, Chenxi Liu, Deepali Aneja, Matthew Fisher, Siqi Wang","submitted_at":"2024-07-15T21:55:19Z","abstract_excerpt":"Effective representation of 2D images is fundamental in digital image processing, where traditional methods like raster and vector graphics struggle with sharpness and textural complexity respectively. Current neural fields offer high-fidelity and resolution independence but require predefined meshes with known discontinuities, restricting their utility. We observe that by treating all mesh edges as potential discontinuities, we can represent the magnitude of discontinuities with continuous variables and optimize. Based on this observation, we introduce a novel discontinuous neural field model"},"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":"2408.00771","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-07-15T21:55:19Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"78cccfdb5d5674bb32aabbedac6c02b87f14c468cc6ed552bac0698c767360b8","abstract_canon_sha256":"fd28ad4f7ff41975b67c5d04613a8f76bd8f831b49e32854c7b897647ca96ae4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:51:21.550328Z","signature_b64":"TJ2OnOOgTwrZemuDT0HRT7d0suuCsGCzqIX8WQ0JXzgLtLFWtOpEpkvllUhD1wJUqWt4DIKQJBG8ses3Eqd5Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5dae2347ebe7454d8b23dcace9f402fad8acf6689d6bfa6670a591ad10fe48fe","last_reissued_at":"2026-07-05T08:51:21.549923Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:51:21.549923Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"2D Neural Fields with Learned Discontinuities","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Alec Jacobson, Chenxi Liu, Deepali Aneja, Matthew Fisher, Siqi Wang","submitted_at":"2024-07-15T21:55:19Z","abstract_excerpt":"Effective representation of 2D images is fundamental in digital image processing, where traditional methods like raster and vector graphics struggle with sharpness and textural complexity respectively. Current neural fields offer high-fidelity and resolution independence but require predefined meshes with known discontinuities, restricting their utility. We observe that by treating all mesh edges as potential discontinuities, we can represent the magnitude of discontinuities with continuous variables and optimize. Based on this observation, we introduce a novel discontinuous neural field model"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.00771","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/2408.00771/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":"2408.00771","created_at":"2026-07-05T08:51:21.549979+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.00771v1","created_at":"2026-07-05T08:51:21.549979+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.00771","created_at":"2026-07-05T08:51:21.549979+00:00"},{"alias_kind":"pith_short_12","alias_value":"LWXCGR7L45CU","created_at":"2026-07-05T08:51:21.549979+00:00"},{"alias_kind":"pith_short_16","alias_value":"LWXCGR7L45CU3CZD","created_at":"2026-07-05T08:51:21.549979+00:00"},{"alias_kind":"pith_short_8","alias_value":"LWXCGR7L","created_at":"2026-07-05T08:51:21.549979+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/LWXCGR7L45CU3CZD3SWOT5AC7L","json":"https://pith.science/pith/LWXCGR7L45CU3CZD3SWOT5AC7L.json","graph_json":"https://pith.science/api/pith-number/LWXCGR7L45CU3CZD3SWOT5AC7L/graph.json","events_json":"https://pith.science/api/pith-number/LWXCGR7L45CU3CZD3SWOT5AC7L/events.json","paper":"https://pith.science/paper/LWXCGR7L"},"agent_actions":{"view_html":"https://pith.science/pith/LWXCGR7L45CU3CZD3SWOT5AC7L","download_json":"https://pith.science/pith/LWXCGR7L45CU3CZD3SWOT5AC7L.json","view_paper":"https://pith.science/paper/LWXCGR7L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.00771&json=true","fetch_graph":"https://pith.science/api/pith-number/LWXCGR7L45CU3CZD3SWOT5AC7L/graph.json","fetch_events":"https://pith.science/api/pith-number/LWXCGR7L45CU3CZD3SWOT5AC7L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LWXCGR7L45CU3CZD3SWOT5AC7L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LWXCGR7L45CU3CZD3SWOT5AC7L/action/storage_attestation","attest_author":"https://pith.science/pith/LWXCGR7L45CU3CZD3SWOT5AC7L/action/author_attestation","sign_citation":"https://pith.science/pith/LWXCGR7L45CU3CZD3SWOT5AC7L/action/citation_signature","submit_replication":"https://pith.science/pith/LWXCGR7L45CU3CZD3SWOT5AC7L/action/replication_record"}},"created_at":"2026-07-05T08:51:21.549979+00:00","updated_at":"2026-07-05T08:51:21.549979+00:00"}