{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:UB2FVEWBRKKXML3ALY5CT3YWS6","short_pith_number":"pith:UB2FVEWB","canonical_record":{"source":{"id":"2012.09439","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2020-12-17T08:20:09Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"f63f105a28ef7194c0247759ee4793df8439b0379164569443082054a9e0e42c","abstract_canon_sha256":"0df19ba2e0d3de3ed310e0ecc55e2bce671ffcfebf7bba7444d4f23ea3da2978"},"schema_version":"1.0"},"canonical_sha256":"a0745a92c18a95762f605e3a29ef169787df3bd7432b567ba1c42654142070c1","source":{"kind":"arxiv","id":"2012.09439","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.09439","created_at":"2026-07-05T02:50:08Z"},{"alias_kind":"arxiv_version","alias_value":"2012.09439v2","created_at":"2026-07-05T02:50:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.09439","created_at":"2026-07-05T02:50:08Z"},{"alias_kind":"pith_short_12","alias_value":"UB2FVEWBRKKX","created_at":"2026-07-05T02:50:08Z"},{"alias_kind":"pith_short_16","alias_value":"UB2FVEWBRKKXML3A","created_at":"2026-07-05T02:50:08Z"},{"alias_kind":"pith_short_8","alias_value":"UB2FVEWB","created_at":"2026-07-05T02:50:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:UB2FVEWBRKKXML3ALY5CT3YWS6","target":"record","payload":{"canonical_record":{"source":{"id":"2012.09439","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2020-12-17T08:20:09Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"f63f105a28ef7194c0247759ee4793df8439b0379164569443082054a9e0e42c","abstract_canon_sha256":"0df19ba2e0d3de3ed310e0ecc55e2bce671ffcfebf7bba7444d4f23ea3da2978"},"schema_version":"1.0"},"canonical_sha256":"a0745a92c18a95762f605e3a29ef169787df3bd7432b567ba1c42654142070c1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:50:08.383418Z","signature_b64":"C9JR0IjQi3EKYjQsZJ0bK93Pb6omE6z9KAA38NlnRzwtNAzaBmP715ePg2QkRNyLplG06MREyLQtPqYA2s+7Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a0745a92c18a95762f605e3a29ef169787df3bd7432b567ba1c42654142070c1","last_reissued_at":"2026-07-05T02:50:08.382938Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:50:08.382938Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2012.09439","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":"2026-07-05T02:50:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OI9Zb/cuazfPyK/iDbw52t0a8AIve/5aLFdawjOsvQaEuDFl79Zx3c9evJ4XL77u9/iHRAhFBsgLkfo+YBmKAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-18T11:02:04.117633Z"},"content_sha256":"8fa1246692e4b1922ad4dc80edba7fdc19be33c8ccc6aae451c5f0bf6e287152","schema_version":"1.0","event_id":"sha256:8fa1246692e4b1922ad4dc80edba7fdc19be33c8ccc6aae451c5f0bf6e287152"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:UB2FVEWBRKKXML3ALY5CT3YWS6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware Modelling","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Ben M. Chen, Feng Lin, Kangcheng Liu, Zhi Gao","submitted_at":"2020-12-17T08:20:09Z","abstract_excerpt":"This work presents FG-Net, a general deep learning framework for large-scale point clouds understanding without voxelizations, which achieves accurate and real-time performance with a single NVIDIA GTX 1080 GPU. First, a novel noise and outlier filtering method is designed to facilitate subsequent high-level tasks. For effective understanding purpose, we propose a deep convolutional neural network leveraging correlated feature mining and deformable convolution based geometric-aware modelling, in which the local feature relationships and geometric patterns can be fully exploited. For the effici"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.09439","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/2012.09439/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":"2026-07-05T02:50:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AB67gBHOKsnKspAFTfk3cHCJ/ErXqrKc/tFykiNOrmIOU9DfiEANM42NVjX0h8tQvtkTU4FUZqyQzcwV92+TAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-18T11:02:04.118017Z"},"content_sha256":"7cb0dc0c49fe31e1ca189abd16cd5449f1a3374e68c75da43e8e9d0c11661d58","schema_version":"1.0","event_id":"sha256:7cb0dc0c49fe31e1ca189abd16cd5449f1a3374e68c75da43e8e9d0c11661d58"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UB2FVEWBRKKXML3ALY5CT3YWS6/bundle.json","state_url":"https://pith.science/pith/UB2FVEWBRKKXML3ALY5CT3YWS6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UB2FVEWBRKKXML3ALY5CT3YWS6/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":"2026-07-18T11:02:04Z","links":{"resolver":"https://pith.science/pith/UB2FVEWBRKKXML3ALY5CT3YWS6","bundle":"https://pith.science/pith/UB2FVEWBRKKXML3ALY5CT3YWS6/bundle.json","state":"https://pith.science/pith/UB2FVEWBRKKXML3ALY5CT3YWS6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UB2FVEWBRKKXML3ALY5CT3YWS6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:UB2FVEWBRKKXML3ALY5CT3YWS6","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":"0df19ba2e0d3de3ed310e0ecc55e2bce671ffcfebf7bba7444d4f23ea3da2978","cross_cats_sorted":["cs.RO"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2020-12-17T08:20:09Z","title_canon_sha256":"f63f105a28ef7194c0247759ee4793df8439b0379164569443082054a9e0e42c"},"schema_version":"1.0","source":{"id":"2012.09439","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.09439","created_at":"2026-07-05T02:50:08Z"},{"alias_kind":"arxiv_version","alias_value":"2012.09439v2","created_at":"2026-07-05T02:50:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.09439","created_at":"2026-07-05T02:50:08Z"},{"alias_kind":"pith_short_12","alias_value":"UB2FVEWBRKKX","created_at":"2026-07-05T02:50:08Z"},{"alias_kind":"pith_short_16","alias_value":"UB2FVEWBRKKXML3A","created_at":"2026-07-05T02:50:08Z"},{"alias_kind":"pith_short_8","alias_value":"UB2FVEWB","created_at":"2026-07-05T02:50:08Z"}],"graph_snapshots":[{"event_id":"sha256:7cb0dc0c49fe31e1ca189abd16cd5449f1a3374e68c75da43e8e9d0c11661d58","target":"graph","created_at":"2026-07-05T02:50:08Z","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/2012.09439/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This work presents FG-Net, a general deep learning framework for large-scale point clouds understanding without voxelizations, which achieves accurate and real-time performance with a single NVIDIA GTX 1080 GPU. First, a novel noise and outlier filtering method is designed to facilitate subsequent high-level tasks. For effective understanding purpose, we propose a deep convolutional neural network leveraging correlated feature mining and deformable convolution based geometric-aware modelling, in which the local feature relationships and geometric patterns can be fully exploited. For the effici","authors_text":"Ben M. Chen, Feng Lin, Kangcheng Liu, Zhi Gao","cross_cats":["cs.RO"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2020-12-17T08:20:09Z","title":"FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware Modelling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.09439","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":"sha256:8fa1246692e4b1922ad4dc80edba7fdc19be33c8ccc6aae451c5f0bf6e287152","target":"record","created_at":"2026-07-05T02:50:08Z","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":"0df19ba2e0d3de3ed310e0ecc55e2bce671ffcfebf7bba7444d4f23ea3da2978","cross_cats_sorted":["cs.RO"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2020-12-17T08:20:09Z","title_canon_sha256":"f63f105a28ef7194c0247759ee4793df8439b0379164569443082054a9e0e42c"},"schema_version":"1.0","source":{"id":"2012.09439","kind":"arxiv","version":2}},"canonical_sha256":"a0745a92c18a95762f605e3a29ef169787df3bd7432b567ba1c42654142070c1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a0745a92c18a95762f605e3a29ef169787df3bd7432b567ba1c42654142070c1","first_computed_at":"2026-07-05T02:50:08.382938Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:50:08.382938Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"C9JR0IjQi3EKYjQsZJ0bK93Pb6omE6z9KAA38NlnRzwtNAzaBmP715ePg2QkRNyLplG06MREyLQtPqYA2s+7Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T02:50:08.383418Z","signed_message":"canonical_sha256_bytes"},"source_id":"2012.09439","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8fa1246692e4b1922ad4dc80edba7fdc19be33c8ccc6aae451c5f0bf6e287152","sha256:7cb0dc0c49fe31e1ca189abd16cd5449f1a3374e68c75da43e8e9d0c11661d58"],"state_sha256":"c2536b789a709221ab605bd1e2e9818ab6d7a2e25bebbf6c331fa7d4a3a05ecc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AfUyPzr1X69H0CrZC0T/VxQEoHob5MXF7IGMR/rp2cW1fCNBt8t9t6SWIAt5zINgUE/nZ0MkEnpguuHNU+/aAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-18T11:02:04.120237Z","bundle_sha256":"bd7fe4d3ec7d078cc5218f3622e8d83466ec4467f3efccb12d0a30ea375f7ff1"}}