{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:QAKMEQQUZMFCBIGHQB23YI3FZU","short_pith_number":"pith:QAKMEQQU","canonical_record":{"source":{"id":"2406.07648","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-11T18:29:13Z","cross_cats_sorted":[],"title_canon_sha256":"b2958de27fc62ab3785f936695ee8b43eaf23318a31b09475e756083f8ab0c0f","abstract_canon_sha256":"97d91eff6811aa3231420c5392ca6f6c7b5f065d08e8c658139d9d383615f969"},"schema_version":"1.0"},"canonical_sha256":"8014c24214cb0a20a0c78075bc2365cd293cf46d6b048b6d1b4b0297faabffa8","source":{"kind":"arxiv","id":"2406.07648","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.07648","created_at":"2026-07-05T09:43:07Z"},{"alias_kind":"arxiv_version","alias_value":"2406.07648v2","created_at":"2026-07-05T09:43:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.07648","created_at":"2026-07-05T09:43:07Z"},{"alias_kind":"pith_short_12","alias_value":"QAKMEQQUZMFC","created_at":"2026-07-05T09:43:07Z"},{"alias_kind":"pith_short_16","alias_value":"QAKMEQQUZMFCBIGH","created_at":"2026-07-05T09:43:07Z"},{"alias_kind":"pith_short_8","alias_value":"QAKMEQQU","created_at":"2026-07-05T09:43:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:QAKMEQQUZMFCBIGHQB23YI3FZU","target":"record","payload":{"canonical_record":{"source":{"id":"2406.07648","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-11T18:29:13Z","cross_cats_sorted":[],"title_canon_sha256":"b2958de27fc62ab3785f936695ee8b43eaf23318a31b09475e756083f8ab0c0f","abstract_canon_sha256":"97d91eff6811aa3231420c5392ca6f6c7b5f065d08e8c658139d9d383615f969"},"schema_version":"1.0"},"canonical_sha256":"8014c24214cb0a20a0c78075bc2365cd293cf46d6b048b6d1b4b0297faabffa8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:43:07.797511Z","signature_b64":"NIgmd2GAopBeFvBXmFu7I2s850BuXrlPHtJM5lk8s70womEsKtMbF5we1M3thupqkKiabeszbEiEXkYtIuTdAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8014c24214cb0a20a0c78075bc2365cd293cf46d6b048b6d1b4b0297faabffa8","last_reissued_at":"2026-07-05T09:43:07.797007Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:43:07.797007Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2406.07648","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:43:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wCda7JetryqPt+sf74jAuTRs4QduhmxuLfdjFSrTqijxl7yBmODIun5Da5WSQo9mujX4tu7V6kCoweWnKBVfDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T12:00:20.265237Z"},"content_sha256":"25d47ee5e91b4f62867223a8ef858d57dbab746c379c591719dc7e72a0f42609","schema_version":"1.0","event_id":"sha256:25d47ee5e91b4f62867223a8ef858d57dbab746c379c591719dc7e72a0f42609"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:QAKMEQQUZMFCBIGHQB23YI3FZU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-View Large Reconstruction Model via Geometry-Aware Positional Encoding and Attention","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Mengfei Li, Peng Li, Weiyu Li, Wenhan Luo, Wenping Wang, Xiaoxiao Long, Yike Guo, Yixun Liang, Yuan Liu","submitted_at":"2024-06-11T18:29:13Z","abstract_excerpt":"Despite recent advancements in the Large Reconstruction Model (LRM) demonstrating impressive results, when extending its input from single image to multiple images, it exhibits inefficiencies, subpar geometric and texture quality, as well as slower convergence speed than expected. It is attributed to that, LRM formulates 3D reconstruction as a naive images-to-3D translation problem, ignoring the strong 3D coherence among the input images. In this paper, we propose a Multi-view Large Reconstruction Model (M-LRM) designed to reconstruct high-quality 3D shapes from multi-views in a 3D-aware manne"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.07648","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/2406.07648/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:43:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"P4mMOdvUEw8WOSfLBC0Q68X4OAtPXy7iAtm6cW37NabNSkMSHMZoHWPmMcNRPxpasc/Xjmdb1u+OQ7yHyMK7CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T12:00:20.266284Z"},"content_sha256":"f81832828a3d132d0b1907af713b5dc5a1070034d0ba4fa6b98c81a004e5b098","schema_version":"1.0","event_id":"sha256:f81832828a3d132d0b1907af713b5dc5a1070034d0ba4fa6b98c81a004e5b098"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QAKMEQQUZMFCBIGHQB23YI3FZU/bundle.json","state_url":"https://pith.science/pith/QAKMEQQUZMFCBIGHQB23YI3FZU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QAKMEQQUZMFCBIGHQB23YI3FZU/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-18T12:00:20Z","links":{"resolver":"https://pith.science/pith/QAKMEQQUZMFCBIGHQB23YI3FZU","bundle":"https://pith.science/pith/QAKMEQQUZMFCBIGHQB23YI3FZU/bundle.json","state":"https://pith.science/pith/QAKMEQQUZMFCBIGHQB23YI3FZU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QAKMEQQUZMFCBIGHQB23YI3FZU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:QAKMEQQUZMFCBIGHQB23YI3FZU","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"97d91eff6811aa3231420c5392ca6f6c7b5f065d08e8c658139d9d383615f969","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-11T18:29:13Z","title_canon_sha256":"b2958de27fc62ab3785f936695ee8b43eaf23318a31b09475e756083f8ab0c0f"},"schema_version":"1.0","source":{"id":"2406.07648","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.07648","created_at":"2026-07-05T09:43:07Z"},{"alias_kind":"arxiv_version","alias_value":"2406.07648v2","created_at":"2026-07-05T09:43:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.07648","created_at":"2026-07-05T09:43:07Z"},{"alias_kind":"pith_short_12","alias_value":"QAKMEQQUZMFC","created_at":"2026-07-05T09:43:07Z"},{"alias_kind":"pith_short_16","alias_value":"QAKMEQQUZMFCBIGH","created_at":"2026-07-05T09:43:07Z"},{"alias_kind":"pith_short_8","alias_value":"QAKMEQQU","created_at":"2026-07-05T09:43:07Z"}],"graph_snapshots":[{"event_id":"sha256:f81832828a3d132d0b1907af713b5dc5a1070034d0ba4fa6b98c81a004e5b098","target":"graph","created_at":"2026-07-05T09:43:07Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2406.07648/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Despite recent advancements in the Large Reconstruction Model (LRM) demonstrating impressive results, when extending its input from single image to multiple images, it exhibits inefficiencies, subpar geometric and texture quality, as well as slower convergence speed than expected. It is attributed to that, LRM formulates 3D reconstruction as a naive images-to-3D translation problem, ignoring the strong 3D coherence among the input images. In this paper, we propose a Multi-view Large Reconstruction Model (M-LRM) designed to reconstruct high-quality 3D shapes from multi-views in a 3D-aware manne","authors_text":"Mengfei Li, Peng Li, Weiyu Li, Wenhan Luo, Wenping Wang, Xiaoxiao Long, Yike Guo, Yixun Liang, Yuan Liu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-11T18:29:13Z","title":"Multi-View Large Reconstruction Model via Geometry-Aware Positional Encoding and Attention"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.07648","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:25d47ee5e91b4f62867223a8ef858d57dbab746c379c591719dc7e72a0f42609","target":"record","created_at":"2026-07-05T09:43:07Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"97d91eff6811aa3231420c5392ca6f6c7b5f065d08e8c658139d9d383615f969","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-11T18:29:13Z","title_canon_sha256":"b2958de27fc62ab3785f936695ee8b43eaf23318a31b09475e756083f8ab0c0f"},"schema_version":"1.0","source":{"id":"2406.07648","kind":"arxiv","version":2}},"canonical_sha256":"8014c24214cb0a20a0c78075bc2365cd293cf46d6b048b6d1b4b0297faabffa8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8014c24214cb0a20a0c78075bc2365cd293cf46d6b048b6d1b4b0297faabffa8","first_computed_at":"2026-07-05T09:43:07.797007Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:43:07.797007Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NIgmd2GAopBeFvBXmFu7I2s850BuXrlPHtJM5lk8s70womEsKtMbF5we1M3thupqkKiabeszbEiEXkYtIuTdAA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:43:07.797511Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.07648","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:25d47ee5e91b4f62867223a8ef858d57dbab746c379c591719dc7e72a0f42609","sha256:f81832828a3d132d0b1907af713b5dc5a1070034d0ba4fa6b98c81a004e5b098"],"state_sha256":"fed46068c25afac7bc92532167c9c064df253c06b1050e4875a77289a6219a4d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1ZO/l0HvFULOnT4hJEZfZvyu6/CHMAgkFFIpZjcgdDc1VcgnOkazaTeBCsLHrZ9wDtfEEbg5ttOBnxmK2Bw/Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T12:00:20.272539Z","bundle_sha256":"b023ab20859ede69b1ea78caf5fa8b80e2a2ab93fb5396d8958636af1471d851"}}