{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:M35FU5WPU5SMQ4XEVFMMAJDMUK","short_pith_number":"pith:M35FU5WP","canonical_record":{"source":{"id":"2507.18522","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-24T15:46:38Z","cross_cats_sorted":[],"title_canon_sha256":"8067232c4a4c59b9cdcf18d14d83f5b525d6d7e7d1a204f1a1e0828c344f6300","abstract_canon_sha256":"edf2869911e0a82395ead210e2a70dfd9bba475c4cf6a3d5c6fb94972d1cebba"},"schema_version":"1.0"},"canonical_sha256":"66fa5a76cfa764c872e4a958c0246ca2b9df2b87b5f7f2c146e4f43c393cc93a","source":{"kind":"arxiv","id":"2507.18522","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.18522","created_at":"2026-07-05T11:42:49Z"},{"alias_kind":"arxiv_version","alias_value":"2507.18522v1","created_at":"2026-07-05T11:42:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.18522","created_at":"2026-07-05T11:42:49Z"},{"alias_kind":"pith_short_12","alias_value":"M35FU5WPU5SM","created_at":"2026-07-05T11:42:49Z"},{"alias_kind":"pith_short_16","alias_value":"M35FU5WPU5SMQ4XE","created_at":"2026-07-05T11:42:49Z"},{"alias_kind":"pith_short_8","alias_value":"M35FU5WP","created_at":"2026-07-05T11:42:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:M35FU5WPU5SMQ4XEVFMMAJDMUK","target":"record","payload":{"canonical_record":{"source":{"id":"2507.18522","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-24T15:46:38Z","cross_cats_sorted":[],"title_canon_sha256":"8067232c4a4c59b9cdcf18d14d83f5b525d6d7e7d1a204f1a1e0828c344f6300","abstract_canon_sha256":"edf2869911e0a82395ead210e2a70dfd9bba475c4cf6a3d5c6fb94972d1cebba"},"schema_version":"1.0"},"canonical_sha256":"66fa5a76cfa764c872e4a958c0246ca2b9df2b87b5f7f2c146e4f43c393cc93a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:42:49.393264Z","signature_b64":"XiHTWFmzXaSl2ftd+oNvWAr+Z5RlGexR4KQVjsROsboLn65yUrBNCeEqFM+DA2mXN6UTNfQWya/lfF6p9YOxBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"66fa5a76cfa764c872e4a958c0246ca2b9df2b87b5f7f2c146e4f43c393cc93a","last_reissued_at":"2026-07-05T11:42:49.392786Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:42:49.392786Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.18522","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":"2026-07-05T11:42:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JJBhrMAbe/D1NW0g3gUZAIedIDZsPxntmDPLdjUJEl5hDvLFFzkfsI6lOo5o2PFB0vRvL4PzCmvU1SFvMWpfAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T20:12:02.490477Z"},"content_sha256":"f13ca8986d6855fd2d3a6b7e4118283c5d408e803d7362adfc88f577f128f39f","schema_version":"1.0","event_id":"sha256:f13ca8986d6855fd2d3a6b7e4118283c5d408e803d7362adfc88f577f128f39f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:M35FU5WPU5SMQ4XEVFMMAJDMUK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"GaussianFusionOcc: A Seamless Sensor Fusion Approach for 3D Occupancy Prediction Using 3D Gaussians","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Johannes Betz, Johannes Niedermayer, Mohammad-Ali Nikouei Mahani, Tomislav Pavkovi\\'c","submitted_at":"2025-07-24T15:46:38Z","abstract_excerpt":"3D semantic occupancy prediction is one of the crucial tasks of autonomous driving. It enables precise and safe interpretation and navigation in complex environments. Reliable predictions rely on effective sensor fusion, as different modalities can contain complementary information. Unlike conventional methods that depend on dense grid representations, our approach, GaussianFusionOcc, uses semantic 3D Gaussians alongside an innovative sensor fusion mechanism. Seamless integration of data from camera, LiDAR, and radar sensors enables more precise and scalable occupancy prediction, while 3D Gaus"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.18522","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/2507.18522/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":"2026-07-05T11:42:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ihxIVSE5E2MOafnUE1TvyPdslZNTdDFXJxmRQvSPgp1Etz+vg5/E7DUvfcMujjmE3+JE2RtCC3K47VAEFq8sBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T20:12:02.491087Z"},"content_sha256":"390005177768f536ec012290fe6f14fa69541974c6bacbd38198f6e0f250b453","schema_version":"1.0","event_id":"sha256:390005177768f536ec012290fe6f14fa69541974c6bacbd38198f6e0f250b453"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/M35FU5WPU5SMQ4XEVFMMAJDMUK/bundle.json","state_url":"https://pith.science/pith/M35FU5WPU5SMQ4XEVFMMAJDMUK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/M35FU5WPU5SMQ4XEVFMMAJDMUK/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":"2026-08-07T20:12:02Z","links":{"resolver":"https://pith.science/pith/M35FU5WPU5SMQ4XEVFMMAJDMUK","bundle":"https://pith.science/pith/M35FU5WPU5SMQ4XEVFMMAJDMUK/bundle.json","state":"https://pith.science/pith/M35FU5WPU5SMQ4XEVFMMAJDMUK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/M35FU5WPU5SMQ4XEVFMMAJDMUK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:M35FU5WPU5SMQ4XEVFMMAJDMUK","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":"edf2869911e0a82395ead210e2a70dfd9bba475c4cf6a3d5c6fb94972d1cebba","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-24T15:46:38Z","title_canon_sha256":"8067232c4a4c59b9cdcf18d14d83f5b525d6d7e7d1a204f1a1e0828c344f6300"},"schema_version":"1.0","source":{"id":"2507.18522","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.18522","created_at":"2026-07-05T11:42:49Z"},{"alias_kind":"arxiv_version","alias_value":"2507.18522v1","created_at":"2026-07-05T11:42:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.18522","created_at":"2026-07-05T11:42:49Z"},{"alias_kind":"pith_short_12","alias_value":"M35FU5WPU5SM","created_at":"2026-07-05T11:42:49Z"},{"alias_kind":"pith_short_16","alias_value":"M35FU5WPU5SMQ4XE","created_at":"2026-07-05T11:42:49Z"},{"alias_kind":"pith_short_8","alias_value":"M35FU5WP","created_at":"2026-07-05T11:42:49Z"}],"graph_snapshots":[{"event_id":"sha256:390005177768f536ec012290fe6f14fa69541974c6bacbd38198f6e0f250b453","target":"graph","created_at":"2026-07-05T11:42:49Z","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/2507.18522/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"3D semantic occupancy prediction is one of the crucial tasks of autonomous driving. It enables precise and safe interpretation and navigation in complex environments. Reliable predictions rely on effective sensor fusion, as different modalities can contain complementary information. Unlike conventional methods that depend on dense grid representations, our approach, GaussianFusionOcc, uses semantic 3D Gaussians alongside an innovative sensor fusion mechanism. Seamless integration of data from camera, LiDAR, and radar sensors enables more precise and scalable occupancy prediction, while 3D Gaus","authors_text":"Johannes Betz, Johannes Niedermayer, Mohammad-Ali Nikouei Mahani, Tomislav Pavkovi\\'c","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-24T15:46:38Z","title":"GaussianFusionOcc: A Seamless Sensor Fusion Approach for 3D Occupancy Prediction Using 3D Gaussians"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.18522","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:f13ca8986d6855fd2d3a6b7e4118283c5d408e803d7362adfc88f577f128f39f","target":"record","created_at":"2026-07-05T11:42:49Z","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":"edf2869911e0a82395ead210e2a70dfd9bba475c4cf6a3d5c6fb94972d1cebba","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-24T15:46:38Z","title_canon_sha256":"8067232c4a4c59b9cdcf18d14d83f5b525d6d7e7d1a204f1a1e0828c344f6300"},"schema_version":"1.0","source":{"id":"2507.18522","kind":"arxiv","version":1}},"canonical_sha256":"66fa5a76cfa764c872e4a958c0246ca2b9df2b87b5f7f2c146e4f43c393cc93a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"66fa5a76cfa764c872e4a958c0246ca2b9df2b87b5f7f2c146e4f43c393cc93a","first_computed_at":"2026-07-05T11:42:49.392786Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:42:49.392786Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XiHTWFmzXaSl2ftd+oNvWAr+Z5RlGexR4KQVjsROsboLn65yUrBNCeEqFM+DA2mXN6UTNfQWya/lfF6p9YOxBw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:42:49.393264Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.18522","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f13ca8986d6855fd2d3a6b7e4118283c5d408e803d7362adfc88f577f128f39f","sha256:390005177768f536ec012290fe6f14fa69541974c6bacbd38198f6e0f250b453"],"state_sha256":"b52e4eb6d83430c53f3625840a02b3ca2a8fe2e1ddf8c1c47e174d7dfd7f4c66"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"S/7/Cmyz7UOCCt7IwfnmQSqPcvklkkiGouzpv/UwRqIXgPsIjvLFxsZiPOOivpk0jyAisiYOhZ7Ss8tNaVySDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T20:12:02.494979Z","bundle_sha256":"52f3fe3820e93beed72d5ccbfdc71e18b80a2609d9cba6df5982aa4a2a9fbeb8"}}