{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:F7HWRGQF7HW7OKNNAP65WKSCYR","short_pith_number":"pith:F7HWRGQF","canonical_record":{"source":{"id":"2501.14896","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-24T19:39:15Z","cross_cats_sorted":[],"title_canon_sha256":"57176fcfab574498ae4f016e536895ebd1188065f94d6fea739f52e042751cb7","abstract_canon_sha256":"3e2ea2c098410830c8e5576631cd28bd15c55b72d40647b368da531a75e36d04"},"schema_version":"1.0"},"canonical_sha256":"2fcf689a05f9edf729ad03fddb2a42c444e67928baaffae1a3d85ab24f2fdec2","source":{"kind":"arxiv","id":"2501.14896","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.14896","created_at":"2026-07-05T10:05:20Z"},{"alias_kind":"arxiv_version","alias_value":"2501.14896v1","created_at":"2026-07-05T10:05:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.14896","created_at":"2026-07-05T10:05:20Z"},{"alias_kind":"pith_short_12","alias_value":"F7HWRGQF7HW7","created_at":"2026-07-05T10:05:20Z"},{"alias_kind":"pith_short_16","alias_value":"F7HWRGQF7HW7OKNN","created_at":"2026-07-05T10:05:20Z"},{"alias_kind":"pith_short_8","alias_value":"F7HWRGQF","created_at":"2026-07-05T10:05:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:F7HWRGQF7HW7OKNNAP65WKSCYR","target":"record","payload":{"canonical_record":{"source":{"id":"2501.14896","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-24T19:39:15Z","cross_cats_sorted":[],"title_canon_sha256":"57176fcfab574498ae4f016e536895ebd1188065f94d6fea739f52e042751cb7","abstract_canon_sha256":"3e2ea2c098410830c8e5576631cd28bd15c55b72d40647b368da531a75e36d04"},"schema_version":"1.0"},"canonical_sha256":"2fcf689a05f9edf729ad03fddb2a42c444e67928baaffae1a3d85ab24f2fdec2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:05:20.410941Z","signature_b64":"kctJAKl7KcPoIeyPAASbrbJXxo14qfZV6BmDvczkLzb9beiOkjt3PNyaSxFd6cmRijzAGSuhEFquptUf4tPmBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2fcf689a05f9edf729ad03fddb2a42c444e67928baaffae1a3d85ab24f2fdec2","last_reissued_at":"2026-07-05T10:05:20.410465Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:05:20.410465Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.14896","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:05:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4DttZY8Kyp3GPSDpIWCUXzpE1m4Qu7DiczKXE/HfojrarXaSVrmu0CK5RQzQyOfWcyO/LI2THubQrR6bIBwVDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T22:37:33.651307Z"},"content_sha256":"766511458f7277bdd13d448206539cf6ee435a2f6e846e84e588105172b97816","schema_version":"1.0","event_id":"sha256:766511458f7277bdd13d448206539cf6ee435a2f6e846e84e588105172b97816"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:F7HWRGQF7HW7OKNNAP65WKSCYR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Glissando-Net: Deep sinGLe vIew category level poSe eStimation ANd 3D recOnstruction","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bo Sun, Gang Hua, Hao Kang, Haoxiang Li, Li Guan, Philippos Mordohai","submitted_at":"2025-01-24T19:39:15Z","abstract_excerpt":"We present a deep learning model, dubbed Glissando-Net, to simultaneously estimate the pose and reconstruct the 3D shape of objects at the category level from a single RGB image. Previous works predominantly focused on either estimating poses(often at the instance level), or reconstructing shapes, but not both. Glissando-Net is composed of two auto-encoders that are jointly trained, one for RGB images and the other for point clouds. We embrace two key design choices in Glissando-Net to achieve a more accurate prediction of the 3D shape and pose of the object given a single RGB image as input. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.14896","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/2501.14896/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":"2026-07-05T10:05:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"V0vMdLeW0kjYFsMX2io0gb9oe3oCSEzJPZrIOdV/5PVoi+Xe86+Tf6bigExpALM8XSiQCYRyACXuLoSwzjr+CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T22:37:33.652180Z"},"content_sha256":"2f3d7b835eea33335ceb22fa69d73cbb6d7456cbcbd818c5ba46a888774c9297","schema_version":"1.0","event_id":"sha256:2f3d7b835eea33335ceb22fa69d73cbb6d7456cbcbd818c5ba46a888774c9297"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/F7HWRGQF7HW7OKNNAP65WKSCYR/bundle.json","state_url":"https://pith.science/pith/F7HWRGQF7HW7OKNNAP65WKSCYR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/F7HWRGQF7HW7OKNNAP65WKSCYR/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":"2026-08-12T22:37:33Z","links":{"resolver":"https://pith.science/pith/F7HWRGQF7HW7OKNNAP65WKSCYR","bundle":"https://pith.science/pith/F7HWRGQF7HW7OKNNAP65WKSCYR/bundle.json","state":"https://pith.science/pith/F7HWRGQF7HW7OKNNAP65WKSCYR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/F7HWRGQF7HW7OKNNAP65WKSCYR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:F7HWRGQF7HW7OKNNAP65WKSCYR","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":"3e2ea2c098410830c8e5576631cd28bd15c55b72d40647b368da531a75e36d04","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-24T19:39:15Z","title_canon_sha256":"57176fcfab574498ae4f016e536895ebd1188065f94d6fea739f52e042751cb7"},"schema_version":"1.0","source":{"id":"2501.14896","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.14896","created_at":"2026-07-05T10:05:20Z"},{"alias_kind":"arxiv_version","alias_value":"2501.14896v1","created_at":"2026-07-05T10:05:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.14896","created_at":"2026-07-05T10:05:20Z"},{"alias_kind":"pith_short_12","alias_value":"F7HWRGQF7HW7","created_at":"2026-07-05T10:05:20Z"},{"alias_kind":"pith_short_16","alias_value":"F7HWRGQF7HW7OKNN","created_at":"2026-07-05T10:05:20Z"},{"alias_kind":"pith_short_8","alias_value":"F7HWRGQF","created_at":"2026-07-05T10:05:20Z"}],"graph_snapshots":[{"event_id":"sha256:2f3d7b835eea33335ceb22fa69d73cbb6d7456cbcbd818c5ba46a888774c9297","target":"graph","created_at":"2026-07-05T10:05:20Z","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/2501.14896/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present a deep learning model, dubbed Glissando-Net, to simultaneously estimate the pose and reconstruct the 3D shape of objects at the category level from a single RGB image. Previous works predominantly focused on either estimating poses(often at the instance level), or reconstructing shapes, but not both. Glissando-Net is composed of two auto-encoders that are jointly trained, one for RGB images and the other for point clouds. We embrace two key design choices in Glissando-Net to achieve a more accurate prediction of the 3D shape and pose of the object given a single RGB image as input. ","authors_text":"Bo Sun, Gang Hua, Hao Kang, Haoxiang Li, Li Guan, Philippos Mordohai","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-24T19:39:15Z","title":"Glissando-Net: Deep sinGLe vIew category level poSe eStimation ANd 3D recOnstruction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.14896","kind":"arxiv","version":1},"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":"sha256:766511458f7277bdd13d448206539cf6ee435a2f6e846e84e588105172b97816","target":"record","created_at":"2026-07-05T10:05:20Z","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":"3e2ea2c098410830c8e5576631cd28bd15c55b72d40647b368da531a75e36d04","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-24T19:39:15Z","title_canon_sha256":"57176fcfab574498ae4f016e536895ebd1188065f94d6fea739f52e042751cb7"},"schema_version":"1.0","source":{"id":"2501.14896","kind":"arxiv","version":1}},"canonical_sha256":"2fcf689a05f9edf729ad03fddb2a42c444e67928baaffae1a3d85ab24f2fdec2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2fcf689a05f9edf729ad03fddb2a42c444e67928baaffae1a3d85ab24f2fdec2","first_computed_at":"2026-07-05T10:05:20.410465Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:05:20.410465Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kctJAKl7KcPoIeyPAASbrbJXxo14qfZV6BmDvczkLzb9beiOkjt3PNyaSxFd6cmRijzAGSuhEFquptUf4tPmBw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:05:20.410941Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.14896","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:766511458f7277bdd13d448206539cf6ee435a2f6e846e84e588105172b97816","sha256:2f3d7b835eea33335ceb22fa69d73cbb6d7456cbcbd818c5ba46a888774c9297"],"state_sha256":"1fe5858e29522502f487af0d71b4157bdd4f336518c7c640a1890b2486d7a60b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"inNolEO3KtPNLMK4m4dvreRdmvzi/NJ/OVdOziqDucPjXumTPrmqE15jwUeRbl2EFZzFo9MGCXsiVfsuf+C4Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T22:37:33.657137Z","bundle_sha256":"3ae1344e22ed70dbd7d213c22b6f159a676def0f494e4cbeecc2833c165fd36e"}}