{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:F3BZ2O6JDNTESP2OMFSWE3KNBH","short_pith_number":"pith:F3BZ2O6J","schema_version":"1.0","canonical_sha256":"2ec39d3bc91b66493f4e6165626d4d09fc082c488b4c71dd7090319ba85fe6ab","source":{"kind":"arxiv","id":"2607.02407","version":1},"attestation_state":"computed","paper":{"title":"Text-Driven 3D Indoor Scene Synthesis in Non-Manhattan Environments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.AI","authors_text":"Hangjun Ye, Jun Liu, Kai Chen, Kun Wang, Li Zhang, Long Chen, Xianhui Meng, Xiaoshuai Hao, Xiuying Chen, Yan Luo, Yongxuan Lv, Yuchen Zhang, Zirui Song","submitted_at":"2026-07-02T16:40:08Z","abstract_excerpt":"Large Language Models (LLMs) have demonstrated remarkable capabilities in 3D indoor synthesis for Manhattan environments. However, existing methods often fail to capture plausible object layout patterns in non-Manhattan settings, primarily because they struggle to model non-orthogonal spatial relationships, leading to high geometric violations and low physical fidelity. To address this challenge, we propose SPG-Layout, a novel text-driven framework designed to generate physically plausible indoor scenes within complex non-Manhattan environments. Specifically, we first utilize statistical prior"},"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":"2607.02407","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-07-02T16:40:08Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"d4aa4f6aea5615553444ed322f966f1fb2c54cab58b39281a77d364014e965af","abstract_canon_sha256":"2f4fe8c1d3429b6ac16f3d2c3900f57bbb5ec7c3d2080884f23e0ff3dd253d43"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-03T01:17:58.354780Z","signature_b64":"UshHW270YSfmYtOZEc5pM5lMoKY6Fya1tdpPULl20blwx8Rb9HxV9oYRhEIAoADFUtRuMqxoeD9yoZofpHKlAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2ec39d3bc91b66493f4e6165626d4d09fc082c488b4c71dd7090319ba85fe6ab","last_reissued_at":"2026-07-03T01:17:58.354372Z","signature_status":"signed_v1","first_computed_at":"2026-07-03T01:17:58.354372Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Text-Driven 3D Indoor Scene Synthesis in Non-Manhattan Environments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.AI","authors_text":"Hangjun Ye, Jun Liu, Kai Chen, Kun Wang, Li Zhang, Long Chen, Xianhui Meng, Xiaoshuai Hao, Xiuying Chen, Yan Luo, Yongxuan Lv, Yuchen Zhang, Zirui Song","submitted_at":"2026-07-02T16:40:08Z","abstract_excerpt":"Large Language Models (LLMs) have demonstrated remarkable capabilities in 3D indoor synthesis for Manhattan environments. However, existing methods often fail to capture plausible object layout patterns in non-Manhattan settings, primarily because they struggle to model non-orthogonal spatial relationships, leading to high geometric violations and low physical fidelity. To address this challenge, we propose SPG-Layout, a novel text-driven framework designed to generate physically plausible indoor scenes within complex non-Manhattan environments. Specifically, we first utilize statistical prior"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.02407","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/2607.02407/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":"2607.02407","created_at":"2026-07-03T01:17:58.354430+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.02407v1","created_at":"2026-07-03T01:17:58.354430+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.02407","created_at":"2026-07-03T01:17:58.354430+00:00"},{"alias_kind":"pith_short_12","alias_value":"F3BZ2O6JDNTE","created_at":"2026-07-03T01:17:58.354430+00:00"},{"alias_kind":"pith_short_16","alias_value":"F3BZ2O6JDNTESP2O","created_at":"2026-07-03T01:17:58.354430+00:00"},{"alias_kind":"pith_short_8","alias_value":"F3BZ2O6J","created_at":"2026-07-03T01:17:58.354430+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/F3BZ2O6JDNTESP2OMFSWE3KNBH","json":"https://pith.science/pith/F3BZ2O6JDNTESP2OMFSWE3KNBH.json","graph_json":"https://pith.science/api/pith-number/F3BZ2O6JDNTESP2OMFSWE3KNBH/graph.json","events_json":"https://pith.science/api/pith-number/F3BZ2O6JDNTESP2OMFSWE3KNBH/events.json","paper":"https://pith.science/paper/F3BZ2O6J"},"agent_actions":{"view_html":"https://pith.science/pith/F3BZ2O6JDNTESP2OMFSWE3KNBH","download_json":"https://pith.science/pith/F3BZ2O6JDNTESP2OMFSWE3KNBH.json","view_paper":"https://pith.science/paper/F3BZ2O6J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.02407&json=true","fetch_graph":"https://pith.science/api/pith-number/F3BZ2O6JDNTESP2OMFSWE3KNBH/graph.json","fetch_events":"https://pith.science/api/pith-number/F3BZ2O6JDNTESP2OMFSWE3KNBH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/F3BZ2O6JDNTESP2OMFSWE3KNBH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/F3BZ2O6JDNTESP2OMFSWE3KNBH/action/storage_attestation","attest_author":"https://pith.science/pith/F3BZ2O6JDNTESP2OMFSWE3KNBH/action/author_attestation","sign_citation":"https://pith.science/pith/F3BZ2O6JDNTESP2OMFSWE3KNBH/action/citation_signature","submit_replication":"https://pith.science/pith/F3BZ2O6JDNTESP2OMFSWE3KNBH/action/replication_record"}},"created_at":"2026-07-03T01:17:58.354430+00:00","updated_at":"2026-07-03T01:17:58.354430+00:00"}