{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:OCGCAD6VHXXRKOWDXUX62AGRFK","short_pith_number":"pith:OCGCAD6V","schema_version":"1.0","canonical_sha256":"708c200fd53def153ac3bd2fed00d12a9e8054dcd625969f3b9ae841eaa00e68","source":{"kind":"arxiv","id":"1901.09280","version":2},"attestation_state":"computed","paper":{"title":"Points2Pix: 3D Point-Cloud to Image Translation using conditional Generative Adversarial Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kai Fischer, Martin Simon, Maximillian P\\\"opperl, Stefan Milz","submitted_at":"2019-01-26T21:17:21Z","abstract_excerpt":"We present the first approach for 3D point-cloud to image translation based on conditional Generative Adversarial Networks (cGAN). The model handles multi-modal information sources from different domains, i.e. raw point-sets and images. The generator is capable of processing three conditions, whereas the point-cloud is encoded as raw point-set and camera projection. An image background patch is used as constraint to bias environmental texturing. A global approximation function within the generator is directly applied on the point-cloud (Point-Net). Hence, the representative learning model inco"},"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":"1901.09280","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-26T21:17:21Z","cross_cats_sorted":[],"title_canon_sha256":"bb7742bc5de5e17d90d2ae3659911edff36f2c47510809103b3846d4d340f172","abstract_canon_sha256":"f55495698285f20cf41b21b99117935db5c39dbcc8a94d3fe0c68163649c5d68"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:04:39.652703Z","signature_b64":"isnt9aHYYzPQozv9XunRLmVhlyjdAx0GomJRYPwY1BNvHYfu+pm1ys0QRiUcgz7DdPknQVlsA5zisHBae3xMDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"708c200fd53def153ac3bd2fed00d12a9e8054dcd625969f3b9ae841eaa00e68","last_reissued_at":"2026-07-05T00:04:39.652235Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:04:39.652235Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Points2Pix: 3D Point-Cloud to Image Translation using conditional Generative Adversarial Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kai Fischer, Martin Simon, Maximillian P\\\"opperl, Stefan Milz","submitted_at":"2019-01-26T21:17:21Z","abstract_excerpt":"We present the first approach for 3D point-cloud to image translation based on conditional Generative Adversarial Networks (cGAN). The model handles multi-modal information sources from different domains, i.e. raw point-sets and images. The generator is capable of processing three conditions, whereas the point-cloud is encoded as raw point-set and camera projection. An image background patch is used as constraint to bias environmental texturing. A global approximation function within the generator is directly applied on the point-cloud (Point-Net). Hence, the representative learning model inco"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.09280","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1901.09280/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":"1901.09280","created_at":"2026-07-05T00:04:39.652297+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.09280v2","created_at":"2026-07-05T00:04:39.652297+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.09280","created_at":"2026-07-05T00:04:39.652297+00:00"},{"alias_kind":"pith_short_12","alias_value":"OCGCAD6VHXXR","created_at":"2026-07-05T00:04:39.652297+00:00"},{"alias_kind":"pith_short_16","alias_value":"OCGCAD6VHXXRKOWD","created_at":"2026-07-05T00:04:39.652297+00:00"},{"alias_kind":"pith_short_8","alias_value":"OCGCAD6V","created_at":"2026-07-05T00:04:39.652297+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.08142","citing_title":"Bootstrapping Corner Cases: High-Resolution Inpainting for Safety Critical Detect and Avoid for Automated Flying","ref_index":8,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OCGCAD6VHXXRKOWDXUX62AGRFK","json":"https://pith.science/pith/OCGCAD6VHXXRKOWDXUX62AGRFK.json","graph_json":"https://pith.science/api/pith-number/OCGCAD6VHXXRKOWDXUX62AGRFK/graph.json","events_json":"https://pith.science/api/pith-number/OCGCAD6VHXXRKOWDXUX62AGRFK/events.json","paper":"https://pith.science/paper/OCGCAD6V"},"agent_actions":{"view_html":"https://pith.science/pith/OCGCAD6VHXXRKOWDXUX62AGRFK","download_json":"https://pith.science/pith/OCGCAD6VHXXRKOWDXUX62AGRFK.json","view_paper":"https://pith.science/paper/OCGCAD6V","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.09280&json=true","fetch_graph":"https://pith.science/api/pith-number/OCGCAD6VHXXRKOWDXUX62AGRFK/graph.json","fetch_events":"https://pith.science/api/pith-number/OCGCAD6VHXXRKOWDXUX62AGRFK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OCGCAD6VHXXRKOWDXUX62AGRFK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OCGCAD6VHXXRKOWDXUX62AGRFK/action/storage_attestation","attest_author":"https://pith.science/pith/OCGCAD6VHXXRKOWDXUX62AGRFK/action/author_attestation","sign_citation":"https://pith.science/pith/OCGCAD6VHXXRKOWDXUX62AGRFK/action/citation_signature","submit_replication":"https://pith.science/pith/OCGCAD6VHXXRKOWDXUX62AGRFK/action/replication_record"}},"created_at":"2026-07-05T00:04:39.652297+00:00","updated_at":"2026-07-05T00:04:39.652297+00:00"}