{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:WUUMCQFSGYRVNNLVXAR6DLM4YS","short_pith_number":"pith:WUUMCQFS","canonical_record":{"source":{"id":"2606.31688","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-30T14:01:52Z","cross_cats_sorted":[],"title_canon_sha256":"7f56a28e2cfb758fc36bbd117938cef80446f1da8f8f55393bbf246663c7cb70","abstract_canon_sha256":"fa614c0bbdf9a718a943efc5ac5f6352f5b071c1347326333232711a96274e66"},"schema_version":"1.0"},"canonical_sha256":"b528c140b2362356b575b823e1ad9cc4abe2c35f0951f8cfc90877f0e3451209","source":{"kind":"arxiv","id":"2606.31688","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.31688","created_at":"2026-07-01T01:18:11Z"},{"alias_kind":"arxiv_version","alias_value":"2606.31688v1","created_at":"2026-07-01T01:18:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.31688","created_at":"2026-07-01T01:18:11Z"},{"alias_kind":"pith_short_12","alias_value":"WUUMCQFSGYRV","created_at":"2026-07-01T01:18:11Z"},{"alias_kind":"pith_short_16","alias_value":"WUUMCQFSGYRVNNLV","created_at":"2026-07-01T01:18:11Z"},{"alias_kind":"pith_short_8","alias_value":"WUUMCQFS","created_at":"2026-07-01T01:18:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:WUUMCQFSGYRVNNLVXAR6DLM4YS","target":"record","payload":{"canonical_record":{"source":{"id":"2606.31688","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-30T14:01:52Z","cross_cats_sorted":[],"title_canon_sha256":"7f56a28e2cfb758fc36bbd117938cef80446f1da8f8f55393bbf246663c7cb70","abstract_canon_sha256":"fa614c0bbdf9a718a943efc5ac5f6352f5b071c1347326333232711a96274e66"},"schema_version":"1.0"},"canonical_sha256":"b528c140b2362356b575b823e1ad9cc4abe2c35f0951f8cfc90877f0e3451209","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-01T01:18:11.700914Z","signature_b64":"ZBOjcAtx1EZ8NLsseBBdjRBGu37iztl3lN/NTO0JGGdZ+HqTbqe/ghOm4Y22cnOfyjn8ZeQmCizGywYlE6GADQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b528c140b2362356b575b823e1ad9cc4abe2c35f0951f8cfc90877f0e3451209","last_reissued_at":"2026-07-01T01:18:11.700470Z","signature_status":"signed_v1","first_computed_at":"2026-07-01T01:18:11.700470Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.31688","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":"2026-07-01T01:18:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"igDk4SscO9Z+gCGUDE7FXBM5wUjDDHJMZLpzCkofl+rQmIa4abIrlCuc62KCfaPjqzU94ew/dfo1LQAfkttCAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-01T11:00:40.436193Z"},"content_sha256":"16cade7f1c0363d333a2504b3fea192cf25f28c81a76d4e964d84c5589ba214f","schema_version":"1.0","event_id":"sha256:16cade7f1c0363d333a2504b3fea192cf25f28c81a76d4e964d84c5589ba214f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:WUUMCQFSGYRVNNLVXAR6DLM4YS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Semantic Occupancy Prediction with Dual Range-Voxel Representation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hui Luo, Lizhao Liu, Mingkui Tan, Qingyao Wu, Sitao Chen, Zhuangwei Zhuang","submitted_at":"2026-06-30T14:01:52Z","abstract_excerpt":"LiDAR-based 3D semantic occupancy prediction, which aims to provide accurate and comprehensive scene representation, is crucial for autonomous driving systems. As point clouds suffer from sparsity and incompleteness, leading to insufficient semantic learning and difficult occupancy perception, existing methods often stack multi-sweep point clouds to obtain dense spatial information. However, such a naive strategy also results in efficiency (e.g., additional computational burden) and robustness (e.g., pose transformation noise) concerns, which hinder their practical applications. In this work, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.31688","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/2606.31688/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":"2026-07-01T01:18:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZlUQNq1yqPqYirwYClfdG4IW8C22aMVjQSZz4Jn6ldmc0MxjzlRuBwN2zjixsRaN7QzTbAWm6PClB2W+PmuJCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-01T11:00:40.436600Z"},"content_sha256":"969a9503c822c1b0b85c88dbdb6d8c9cc629b53be59312a0c2a885b4ce66eafd","schema_version":"1.0","event_id":"sha256:969a9503c822c1b0b85c88dbdb6d8c9cc629b53be59312a0c2a885b4ce66eafd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WUUMCQFSGYRVNNLVXAR6DLM4YS/bundle.json","state_url":"https://pith.science/pith/WUUMCQFSGYRVNNLVXAR6DLM4YS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WUUMCQFSGYRVNNLVXAR6DLM4YS/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":"2026-07-01T11:00:40Z","links":{"resolver":"https://pith.science/pith/WUUMCQFSGYRVNNLVXAR6DLM4YS","bundle":"https://pith.science/pith/WUUMCQFSGYRVNNLVXAR6DLM4YS/bundle.json","state":"https://pith.science/pith/WUUMCQFSGYRVNNLVXAR6DLM4YS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WUUMCQFSGYRVNNLVXAR6DLM4YS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:WUUMCQFSGYRVNNLVXAR6DLM4YS","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":"fa614c0bbdf9a718a943efc5ac5f6352f5b071c1347326333232711a96274e66","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-30T14:01:52Z","title_canon_sha256":"7f56a28e2cfb758fc36bbd117938cef80446f1da8f8f55393bbf246663c7cb70"},"schema_version":"1.0","source":{"id":"2606.31688","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.31688","created_at":"2026-07-01T01:18:11Z"},{"alias_kind":"arxiv_version","alias_value":"2606.31688v1","created_at":"2026-07-01T01:18:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.31688","created_at":"2026-07-01T01:18:11Z"},{"alias_kind":"pith_short_12","alias_value":"WUUMCQFSGYRV","created_at":"2026-07-01T01:18:11Z"},{"alias_kind":"pith_short_16","alias_value":"WUUMCQFSGYRVNNLV","created_at":"2026-07-01T01:18:11Z"},{"alias_kind":"pith_short_8","alias_value":"WUUMCQFS","created_at":"2026-07-01T01:18:11Z"}],"graph_snapshots":[{"event_id":"sha256:969a9503c822c1b0b85c88dbdb6d8c9cc629b53be59312a0c2a885b4ce66eafd","target":"graph","created_at":"2026-07-01T01:18:11Z","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/2606.31688/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"LiDAR-based 3D semantic occupancy prediction, which aims to provide accurate and comprehensive scene representation, is crucial for autonomous driving systems. As point clouds suffer from sparsity and incompleteness, leading to insufficient semantic learning and difficult occupancy perception, existing methods often stack multi-sweep point clouds to obtain dense spatial information. However, such a naive strategy also results in efficiency (e.g., additional computational burden) and robustness (e.g., pose transformation noise) concerns, which hinder their practical applications. In this work, ","authors_text":"Hui Luo, Lizhao Liu, Mingkui Tan, Qingyao Wu, Sitao Chen, Zhuangwei Zhuang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-30T14:01:52Z","title":"Semantic Occupancy Prediction with Dual Range-Voxel Representation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.31688","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":"sha256:16cade7f1c0363d333a2504b3fea192cf25f28c81a76d4e964d84c5589ba214f","target":"record","created_at":"2026-07-01T01:18:11Z","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":"fa614c0bbdf9a718a943efc5ac5f6352f5b071c1347326333232711a96274e66","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-30T14:01:52Z","title_canon_sha256":"7f56a28e2cfb758fc36bbd117938cef80446f1da8f8f55393bbf246663c7cb70"},"schema_version":"1.0","source":{"id":"2606.31688","kind":"arxiv","version":1}},"canonical_sha256":"b528c140b2362356b575b823e1ad9cc4abe2c35f0951f8cfc90877f0e3451209","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b528c140b2362356b575b823e1ad9cc4abe2c35f0951f8cfc90877f0e3451209","first_computed_at":"2026-07-01T01:18:11.700470Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-01T01:18:11.700470Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZBOjcAtx1EZ8NLsseBBdjRBGu37iztl3lN/NTO0JGGdZ+HqTbqe/ghOm4Y22cnOfyjn8ZeQmCizGywYlE6GADQ==","signature_status":"signed_v1","signed_at":"2026-07-01T01:18:11.700914Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.31688","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:16cade7f1c0363d333a2504b3fea192cf25f28c81a76d4e964d84c5589ba214f","sha256:969a9503c822c1b0b85c88dbdb6d8c9cc629b53be59312a0c2a885b4ce66eafd"],"state_sha256":"f227b7accf4600294a7281c53f887029b785ca382caedb7ef664fcde6ac28c07"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ENC9a9A6I6gOl7AzsUkgZLW0b2UpL9am0CIsR3aa9WBVVOjVGzMtBb4HL7SARPQ28kWY4ujqqjqTRljV8KB0CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-01T11:00:40.438725Z","bundle_sha256":"7dbb895c7ea8c7c60c70e63a472c13a9ab5b2202091550037b9869f81a469f70"}}