{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:RATWFQJ44EBC6ABOBA27N3WSOQ","short_pith_number":"pith:RATWFQJ4","schema_version":"1.0","canonical_sha256":"882762c13ce1022f002e0835f6eed27433f94bed5cd903c50c49df82cd1eff2c","source":{"kind":"arxiv","id":"2108.08477","version":1},"attestation_state":"computed","paper":{"title":"Image2Lego: Customized LEGO Set Generation from Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Alexander O'Brien, Iddo Drori, Katharina Fransen, Kyle Lennon, Matthew Beveridge, Nikhil Singh, Yamin Arefeen, Yumeng Cao","submitted_at":"2021-08-19T03:42:58Z","abstract_excerpt":"Although LEGO sets have entertained generations of children and adults, the challenge of designing customized builds matching the complexity of real-world or imagined scenes remains too great for the average enthusiast. In order to make this feat possible, we implement a system that generates a LEGO brick model from 2D images. We design a novel solution to this problem that uses an octree-structured autoencoder trained on 3D voxelized models to obtain a feasible latent representation for model reconstruction, and a separate network trained to predict this latent representation from 2D images. "},"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":"2108.08477","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-08-19T03:42:58Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ead97671a39679753db5995a16b4f8d817626eb92434a26f4a4a22a19657c88c","abstract_canon_sha256":"6b738c81b269e848908b93f838b404f83461cb83e80446772449642091b63851"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:07:10.293049Z","signature_b64":"Am3z+ZyAiQsg+nomDFpza6qiigAo/BbSsg8KWI+cCI1Z0qh1x3cDUABoJoqUEa+RDDrR1TJwQdwcdwzpeEkRDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"882762c13ce1022f002e0835f6eed27433f94bed5cd903c50c49df82cd1eff2c","last_reissued_at":"2026-07-05T03:07:10.292572Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:07:10.292572Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Image2Lego: Customized LEGO Set Generation from Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Alexander O'Brien, Iddo Drori, Katharina Fransen, Kyle Lennon, Matthew Beveridge, Nikhil Singh, Yamin Arefeen, Yumeng Cao","submitted_at":"2021-08-19T03:42:58Z","abstract_excerpt":"Although LEGO sets have entertained generations of children and adults, the challenge of designing customized builds matching the complexity of real-world or imagined scenes remains too great for the average enthusiast. In order to make this feat possible, we implement a system that generates a LEGO brick model from 2D images. We design a novel solution to this problem that uses an octree-structured autoencoder trained on 3D voxelized models to obtain a feasible latent representation for model reconstruction, and a separate network trained to predict this latent representation from 2D images. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.08477","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/2108.08477/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":"2108.08477","created_at":"2026-07-05T03:07:10.292631+00:00"},{"alias_kind":"arxiv_version","alias_value":"2108.08477v1","created_at":"2026-07-05T03:07:10.292631+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.08477","created_at":"2026-07-05T03:07:10.292631+00:00"},{"alias_kind":"pith_short_12","alias_value":"RATWFQJ44EBC","created_at":"2026-07-05T03:07:10.292631+00:00"},{"alias_kind":"pith_short_16","alias_value":"RATWFQJ44EBC6ABO","created_at":"2026-07-05T03:07:10.292631+00:00"},{"alias_kind":"pith_short_8","alias_value":"RATWFQJ4","created_at":"2026-07-05T03:07:10.292631+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.26182","citing_title":"BrickAnything: Geometry-Conditioned Buildable Brick Generation with Structure-Aware Tokenization","ref_index":24,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RATWFQJ44EBC6ABOBA27N3WSOQ","json":"https://pith.science/pith/RATWFQJ44EBC6ABOBA27N3WSOQ.json","graph_json":"https://pith.science/api/pith-number/RATWFQJ44EBC6ABOBA27N3WSOQ/graph.json","events_json":"https://pith.science/api/pith-number/RATWFQJ44EBC6ABOBA27N3WSOQ/events.json","paper":"https://pith.science/paper/RATWFQJ4"},"agent_actions":{"view_html":"https://pith.science/pith/RATWFQJ44EBC6ABOBA27N3WSOQ","download_json":"https://pith.science/pith/RATWFQJ44EBC6ABOBA27N3WSOQ.json","view_paper":"https://pith.science/paper/RATWFQJ4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2108.08477&json=true","fetch_graph":"https://pith.science/api/pith-number/RATWFQJ44EBC6ABOBA27N3WSOQ/graph.json","fetch_events":"https://pith.science/api/pith-number/RATWFQJ44EBC6ABOBA27N3WSOQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RATWFQJ44EBC6ABOBA27N3WSOQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RATWFQJ44EBC6ABOBA27N3WSOQ/action/storage_attestation","attest_author":"https://pith.science/pith/RATWFQJ44EBC6ABOBA27N3WSOQ/action/author_attestation","sign_citation":"https://pith.science/pith/RATWFQJ44EBC6ABOBA27N3WSOQ/action/citation_signature","submit_replication":"https://pith.science/pith/RATWFQJ44EBC6ABOBA27N3WSOQ/action/replication_record"}},"created_at":"2026-07-05T03:07:10.292631+00:00","updated_at":"2026-07-05T03:07:10.292631+00:00"}