{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:FWSZHSG3YNKXH6BQUEHUBKF3YH","short_pith_number":"pith:FWSZHSG3","schema_version":"1.0","canonical_sha256":"2da593c8dbc35573f830a10f40a8bbc1d1d5fb1dcf5d7f2bdc1b853d7d2750f9","source":{"kind":"arxiv","id":"2106.03135","version":3},"attestation_state":"computed","paper":{"title":"Go with the Flows: Mixtures of Normalizing Flows for Point Cloud Generation and Reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Federico Tombari, Janis Postels, Luc Van Gool, Mengya Liu, Riccardo Spezialetti","submitted_at":"2021-06-06T14:25:45Z","abstract_excerpt":"Recently normalizing flows (NFs) have demonstrated state-of-the-art performance on modeling 3D point clouds while allowing sampling with arbitrary resolution at inference time. However, these flow-based models still require long training times and large models for representing complicated geometries. This work enhances their representational power by applying mixtures of NFs to point clouds. We show that in this more general framework each component learns to specialize in a particular subregion of an object in a completely unsupervised fashion. By instantiating each mixture component with a c"},"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":"2106.03135","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-06-06T14:25:45Z","cross_cats_sorted":[],"title_canon_sha256":"e0d44e9a4bf2f584863a3ab64eabcfc299b661348f719b8d5138f0dec85f1690","abstract_canon_sha256":"c1dd5940ffd871adc62cd225badbeb8d1d6a484d23d092e72cd9adc009f22bae"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:36:04.987508Z","signature_b64":"7Pg8Pq2C69LpDtiAEFOjpWi19ivcdSt14nO2QBGvgTAwi9XTt+dahq7tE5sxmGujm96P9Z0KkzHwHSaCWCFnDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2da593c8dbc35573f830a10f40a8bbc1d1d5fb1dcf5d7f2bdc1b853d7d2750f9","last_reissued_at":"2026-07-05T03:36:04.986940Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:36:04.986940Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Go with the Flows: Mixtures of Normalizing Flows for Point Cloud Generation and Reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Federico Tombari, Janis Postels, Luc Van Gool, Mengya Liu, Riccardo Spezialetti","submitted_at":"2021-06-06T14:25:45Z","abstract_excerpt":"Recently normalizing flows (NFs) have demonstrated state-of-the-art performance on modeling 3D point clouds while allowing sampling with arbitrary resolution at inference time. However, these flow-based models still require long training times and large models for representing complicated geometries. This work enhances their representational power by applying mixtures of NFs to point clouds. We show that in this more general framework each component learns to specialize in a particular subregion of an object in a completely unsupervised fashion. By instantiating each mixture component with a c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.03135","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2106.03135/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":"2106.03135","created_at":"2026-07-05T03:36:04.986998+00:00"},{"alias_kind":"arxiv_version","alias_value":"2106.03135v3","created_at":"2026-07-05T03:36:04.986998+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.03135","created_at":"2026-07-05T03:36:04.986998+00:00"},{"alias_kind":"pith_short_12","alias_value":"FWSZHSG3YNKX","created_at":"2026-07-05T03:36:04.986998+00:00"},{"alias_kind":"pith_short_16","alias_value":"FWSZHSG3YNKXH6BQ","created_at":"2026-07-05T03:36:04.986998+00:00"},{"alias_kind":"pith_short_8","alias_value":"FWSZHSG3","created_at":"2026-07-05T03:36:04.986998+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/FWSZHSG3YNKXH6BQUEHUBKF3YH","json":"https://pith.science/pith/FWSZHSG3YNKXH6BQUEHUBKF3YH.json","graph_json":"https://pith.science/api/pith-number/FWSZHSG3YNKXH6BQUEHUBKF3YH/graph.json","events_json":"https://pith.science/api/pith-number/FWSZHSG3YNKXH6BQUEHUBKF3YH/events.json","paper":"https://pith.science/paper/FWSZHSG3"},"agent_actions":{"view_html":"https://pith.science/pith/FWSZHSG3YNKXH6BQUEHUBKF3YH","download_json":"https://pith.science/pith/FWSZHSG3YNKXH6BQUEHUBKF3YH.json","view_paper":"https://pith.science/paper/FWSZHSG3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2106.03135&json=true","fetch_graph":"https://pith.science/api/pith-number/FWSZHSG3YNKXH6BQUEHUBKF3YH/graph.json","fetch_events":"https://pith.science/api/pith-number/FWSZHSG3YNKXH6BQUEHUBKF3YH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FWSZHSG3YNKXH6BQUEHUBKF3YH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FWSZHSG3YNKXH6BQUEHUBKF3YH/action/storage_attestation","attest_author":"https://pith.science/pith/FWSZHSG3YNKXH6BQUEHUBKF3YH/action/author_attestation","sign_citation":"https://pith.science/pith/FWSZHSG3YNKXH6BQUEHUBKF3YH/action/citation_signature","submit_replication":"https://pith.science/pith/FWSZHSG3YNKXH6BQUEHUBKF3YH/action/replication_record"}},"created_at":"2026-07-05T03:36:04.986998+00:00","updated_at":"2026-07-05T03:36:04.986998+00:00"}