{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:YESFAEI3ASJFK6DOTOH4IXTV54","short_pith_number":"pith:YESFAEI3","canonical_record":{"source":{"id":"2405.20721","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-05-31T09:23:39Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"faddbd83963e60559e1d1bb88848990af517af857e9edc6085ca19ccb3515969","abstract_canon_sha256":"06820db3d01aeea5f8acdba6bc2e79e94d43780587c5da18679acf1e4823de7a"},"schema_version":"1.0"},"canonical_sha256":"c12450111b049255786e9b8fc45e75ef2d52559981e754078a2c0a05f979c3d6","source":{"kind":"arxiv","id":"2405.20721","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.20721","created_at":"2026-07-05T08:25:41Z"},{"alias_kind":"arxiv_version","alias_value":"2405.20721v1","created_at":"2026-07-05T08:25:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.20721","created_at":"2026-07-05T08:25:41Z"},{"alias_kind":"pith_short_12","alias_value":"YESFAEI3ASJF","created_at":"2026-07-05T08:25:41Z"},{"alias_kind":"pith_short_16","alias_value":"YESFAEI3ASJFK6DO","created_at":"2026-07-05T08:25:41Z"},{"alias_kind":"pith_short_8","alias_value":"YESFAEI3","created_at":"2026-07-05T08:25:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:YESFAEI3ASJFK6DOTOH4IXTV54","target":"record","payload":{"canonical_record":{"source":{"id":"2405.20721","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-05-31T09:23:39Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"faddbd83963e60559e1d1bb88848990af517af857e9edc6085ca19ccb3515969","abstract_canon_sha256":"06820db3d01aeea5f8acdba6bc2e79e94d43780587c5da18679acf1e4823de7a"},"schema_version":"1.0"},"canonical_sha256":"c12450111b049255786e9b8fc45e75ef2d52559981e754078a2c0a05f979c3d6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:25:41.482655Z","signature_b64":"XN1dN3yojD3Unf2aLPTuMN1ycC4ssOH+l1F+IQOr0CZxKaomfInYlApdIl6KgzuCXo6imx8LT3cz/U/s1/0fDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c12450111b049255786e9b8fc45e75ef2d52559981e754078a2c0a05f979c3d6","last_reissued_at":"2026-07-05T08:25:41.482232Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:25:41.482232Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.20721","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":"2026-07-05T08:25:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eXxaM26rTDLPf+EqKWieq7uISyAb8yVr189EJPWjwh67H+/EoldjNd5XnzMprmxjf6dLyx7A5w0k5qpdIc7dAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T08:42:28.654837Z"},"content_sha256":"f79c686b9fe74c2729bb6d17df8b181ce26f35009d080f70a7e4a02f05afb878","schema_version":"1.0","event_id":"sha256:f79c686b9fe74c2729bb6d17df8b181ce26f35009d080f70a7e4a02f05afb878"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:YESFAEI3ASJFK6DOTOH4IXTV54","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ContextGS: Compact 3D Gaussian Splatting with Anchor Level Context Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Alex C. Kot, Bihan Wen, Lanqing Guo, Wenhan Yang, Yufei Wang, Zhihao Li","submitted_at":"2024-05-31T09:23:39Z","abstract_excerpt":"Recently, 3D Gaussian Splatting (3DGS) has become a promising framework for novel view synthesis, offering fast rendering speeds and high fidelity. However, the large number of Gaussians and their associated attributes require effective compression techniques. Existing methods primarily compress neural Gaussians individually and independently, i.e., coding all the neural Gaussians at the same time, with little design for their interactions and spatial dependence. Inspired by the effectiveness of the context model in image compression, we propose the first autoregressive model at the anchor lev"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.20721","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/2405.20721/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":"2026-07-05T08:25:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qZUBdZ+2bIzVt2CAOWUvICMm7z/1s3DN8aL/LKL331iLdta/zIo5LKxd3wjOLU0TpyDNPQHH93le3Yyb1LT3Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T08:42:28.655200Z"},"content_sha256":"100e21debd837e641d7b4364e032ce23ce2f34bfa1327e4f71246fc85d9252ac","schema_version":"1.0","event_id":"sha256:100e21debd837e641d7b4364e032ce23ce2f34bfa1327e4f71246fc85d9252ac"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YESFAEI3ASJFK6DOTOH4IXTV54/bundle.json","state_url":"https://pith.science/pith/YESFAEI3ASJFK6DOTOH4IXTV54/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YESFAEI3ASJFK6DOTOH4IXTV54/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":"2026-07-07T08:42:28Z","links":{"resolver":"https://pith.science/pith/YESFAEI3ASJFK6DOTOH4IXTV54","bundle":"https://pith.science/pith/YESFAEI3ASJFK6DOTOH4IXTV54/bundle.json","state":"https://pith.science/pith/YESFAEI3ASJFK6DOTOH4IXTV54/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YESFAEI3ASJFK6DOTOH4IXTV54/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:YESFAEI3ASJFK6DOTOH4IXTV54","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":"06820db3d01aeea5f8acdba6bc2e79e94d43780587c5da18679acf1e4823de7a","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-05-31T09:23:39Z","title_canon_sha256":"faddbd83963e60559e1d1bb88848990af517af857e9edc6085ca19ccb3515969"},"schema_version":"1.0","source":{"id":"2405.20721","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.20721","created_at":"2026-07-05T08:25:41Z"},{"alias_kind":"arxiv_version","alias_value":"2405.20721v1","created_at":"2026-07-05T08:25:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.20721","created_at":"2026-07-05T08:25:41Z"},{"alias_kind":"pith_short_12","alias_value":"YESFAEI3ASJF","created_at":"2026-07-05T08:25:41Z"},{"alias_kind":"pith_short_16","alias_value":"YESFAEI3ASJFK6DO","created_at":"2026-07-05T08:25:41Z"},{"alias_kind":"pith_short_8","alias_value":"YESFAEI3","created_at":"2026-07-05T08:25:41Z"}],"graph_snapshots":[{"event_id":"sha256:100e21debd837e641d7b4364e032ce23ce2f34bfa1327e4f71246fc85d9252ac","target":"graph","created_at":"2026-07-05T08:25:41Z","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/2405.20721/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recently, 3D Gaussian Splatting (3DGS) has become a promising framework for novel view synthesis, offering fast rendering speeds and high fidelity. However, the large number of Gaussians and their associated attributes require effective compression techniques. Existing methods primarily compress neural Gaussians individually and independently, i.e., coding all the neural Gaussians at the same time, with little design for their interactions and spatial dependence. Inspired by the effectiveness of the context model in image compression, we propose the first autoregressive model at the anchor lev","authors_text":"Alex C. Kot, Bihan Wen, Lanqing Guo, Wenhan Yang, Yufei Wang, Zhihao Li","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-05-31T09:23:39Z","title":"ContextGS: Compact 3D Gaussian Splatting with Anchor Level Context Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.20721","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:f79c686b9fe74c2729bb6d17df8b181ce26f35009d080f70a7e4a02f05afb878","target":"record","created_at":"2026-07-05T08:25:41Z","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":"06820db3d01aeea5f8acdba6bc2e79e94d43780587c5da18679acf1e4823de7a","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-05-31T09:23:39Z","title_canon_sha256":"faddbd83963e60559e1d1bb88848990af517af857e9edc6085ca19ccb3515969"},"schema_version":"1.0","source":{"id":"2405.20721","kind":"arxiv","version":1}},"canonical_sha256":"c12450111b049255786e9b8fc45e75ef2d52559981e754078a2c0a05f979c3d6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c12450111b049255786e9b8fc45e75ef2d52559981e754078a2c0a05f979c3d6","first_computed_at":"2026-07-05T08:25:41.482232Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:25:41.482232Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XN1dN3yojD3Unf2aLPTuMN1ycC4ssOH+l1F+IQOr0CZxKaomfInYlApdIl6KgzuCXo6imx8LT3cz/U/s1/0fDg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:25:41.482655Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.20721","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f79c686b9fe74c2729bb6d17df8b181ce26f35009d080f70a7e4a02f05afb878","sha256:100e21debd837e641d7b4364e032ce23ce2f34bfa1327e4f71246fc85d9252ac"],"state_sha256":"9b2ee53521c32857a401a67dc093c29426fd9f953b43ca707738e1ae99dae1ac"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sLG34fKOY7+Mryxczqy/LfqwHMq6P8v8hEo9142Gbwo4VCDY8H6gdOwuiVU9q7NBgtoIUN7ZURZDwE+jYyNbBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T08:42:28.657088Z","bundle_sha256":"6f5046af84a33ec6df343c4cc87fa7d136ce05a4a64bcf5e62d54a841e0bea8c"}}