{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:Y3HP7VABVXAD3QGWDHUJ246CUS","short_pith_number":"pith:Y3HP7VAB","canonical_record":{"source":{"id":"1709.02128","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2017-09-07T08:13:36Z","cross_cats_sorted":[],"title_canon_sha256":"1c92b71fa3918cada75c36e7927d6203f770abbfd27d88503b070594fd276664","abstract_canon_sha256":"9f8db8dff7ff97dad6a0fdc069ec15c083947b4c6095f100d8e8d438805ba01d"},"schema_version":"1.0"},"canonical_sha256":"c6ceffd401adc03dc0d619e89d73c2a49304e16b96c67c918a9dc7c93311170d","source":{"kind":"arxiv","id":"1709.02128","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.02128","created_at":"2026-05-18T00:35:50Z"},{"alias_kind":"arxiv_version","alias_value":"1709.02128v1","created_at":"2026-05-18T00:35:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.02128","created_at":"2026-05-18T00:35:50Z"},{"alias_kind":"pith_short_12","alias_value":"Y3HP7VABVXAD","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_16","alias_value":"Y3HP7VABVXAD3QGW","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_8","alias_value":"Y3HP7VAB","created_at":"2026-05-18T12:31:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:Y3HP7VABVXAD3QGWDHUJ246CUS","target":"record","payload":{"canonical_record":{"source":{"id":"1709.02128","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2017-09-07T08:13:36Z","cross_cats_sorted":[],"title_canon_sha256":"1c92b71fa3918cada75c36e7927d6203f770abbfd27d88503b070594fd276664","abstract_canon_sha256":"9f8db8dff7ff97dad6a0fdc069ec15c083947b4c6095f100d8e8d438805ba01d"},"schema_version":"1.0"},"canonical_sha256":"c6ceffd401adc03dc0d619e89d73c2a49304e16b96c67c918a9dc7c93311170d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:35:50.076726Z","signature_b64":"mhDOzhaGoyiXMHy3uyN4rNEm7cL6QCWdQF7p3EuAK5tXz580y4t03saoWQ7Oj3iiSRdVO/ot59i+W6FaFBmoAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c6ceffd401adc03dc0d619e89d73c2a49304e16b96c67c918a9dc7c93311170d","last_reissued_at":"2026-05-18T00:35:50.076188Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:35:50.076188Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1709.02128","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":"2026-05-18T00:35:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oFLQgbq/Mv0A2NISIyRhbKj3CQRJOFqxB3lVTnT0gZTlJ0CQAqgbgm8jSBaJECk99mlrSRJ8NyE+WML188MQAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T10:21:34.460845Z"},"content_sha256":"574c13fcf659bc03e1a3fb31017a92236ecdc9cf2fc872d4949a8fc18b983fe1","schema_version":"1.0","event_id":"sha256:574c13fcf659bc03e1a3fb31017a92236ecdc9cf2fc872d4949a8fc18b983fe1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:Y3HP7VABVXAD3QGWDHUJ246CUS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CNN for Very Fast Ground Segmentation in Velodyne LiDAR Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Adam Herout, Martin Velas, Michal Hradis, Michal Spanel","submitted_at":"2017-09-07T08:13:36Z","abstract_excerpt":"This paper presents a novel method for ground segmentation in Velodyne point clouds. We propose an encoding of sparse 3D data from the Velodyne sensor suitable for training a convolutional neural network (CNN). This general purpose approach is used for segmentation of the sparse point cloud into ground and non-ground points. The LiDAR data are represented as a multi-channel 2D signal where the horizontal axis corresponds to the rotation angle and the vertical axis the indexes channels (i.e. laser beams). Multiple topologies of relatively shallow CNNs (i.e. 3-5 convolutional layers) are trained"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.02128","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":""},"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":"2026-05-18T00:35:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BZP/gMOnoU7ydWmy6XhJeE+R/E/LlALyeP2Imyru4XwyWdVkdQhG3gqwsJebylEfAGCb+ULeprx8gr0G0OYmAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T10:21:34.461190Z"},"content_sha256":"48602ec9611b04d8920e36964c512fa4e06448fdcf876b9feb9e45dfb410b5cb","schema_version":"1.0","event_id":"sha256:48602ec9611b04d8920e36964c512fa4e06448fdcf876b9feb9e45dfb410b5cb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Y3HP7VABVXAD3QGWDHUJ246CUS/bundle.json","state_url":"https://pith.science/pith/Y3HP7VABVXAD3QGWDHUJ246CUS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Y3HP7VABVXAD3QGWDHUJ246CUS/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":"2026-05-20T10:21:34Z","links":{"resolver":"https://pith.science/pith/Y3HP7VABVXAD3QGWDHUJ246CUS","bundle":"https://pith.science/pith/Y3HP7VABVXAD3QGWDHUJ246CUS/bundle.json","state":"https://pith.science/pith/Y3HP7VABVXAD3QGWDHUJ246CUS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Y3HP7VABVXAD3QGWDHUJ246CUS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:Y3HP7VABVXAD3QGWDHUJ246CUS","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":"9f8db8dff7ff97dad6a0fdc069ec15c083947b4c6095f100d8e8d438805ba01d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2017-09-07T08:13:36Z","title_canon_sha256":"1c92b71fa3918cada75c36e7927d6203f770abbfd27d88503b070594fd276664"},"schema_version":"1.0","source":{"id":"1709.02128","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.02128","created_at":"2026-05-18T00:35:50Z"},{"alias_kind":"arxiv_version","alias_value":"1709.02128v1","created_at":"2026-05-18T00:35:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.02128","created_at":"2026-05-18T00:35:50Z"},{"alias_kind":"pith_short_12","alias_value":"Y3HP7VABVXAD","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_16","alias_value":"Y3HP7VABVXAD3QGW","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_8","alias_value":"Y3HP7VAB","created_at":"2026-05-18T12:31:56Z"}],"graph_snapshots":[{"event_id":"sha256:48602ec9611b04d8920e36964c512fa4e06448fdcf876b9feb9e45dfb410b5cb","target":"graph","created_at":"2026-05-18T00:35:50Z","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"},"paper":{"abstract_excerpt":"This paper presents a novel method for ground segmentation in Velodyne point clouds. We propose an encoding of sparse 3D data from the Velodyne sensor suitable for training a convolutional neural network (CNN). This general purpose approach is used for segmentation of the sparse point cloud into ground and non-ground points. The LiDAR data are represented as a multi-channel 2D signal where the horizontal axis corresponds to the rotation angle and the vertical axis the indexes channels (i.e. laser beams). Multiple topologies of relatively shallow CNNs (i.e. 3-5 convolutional layers) are trained","authors_text":"Adam Herout, Martin Velas, Michal Hradis, Michal Spanel","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2017-09-07T08:13:36Z","title":"CNN for Very Fast Ground Segmentation in Velodyne LiDAR Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.02128","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":"sha256:574c13fcf659bc03e1a3fb31017a92236ecdc9cf2fc872d4949a8fc18b983fe1","target":"record","created_at":"2026-05-18T00:35:50Z","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":"9f8db8dff7ff97dad6a0fdc069ec15c083947b4c6095f100d8e8d438805ba01d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2017-09-07T08:13:36Z","title_canon_sha256":"1c92b71fa3918cada75c36e7927d6203f770abbfd27d88503b070594fd276664"},"schema_version":"1.0","source":{"id":"1709.02128","kind":"arxiv","version":1}},"canonical_sha256":"c6ceffd401adc03dc0d619e89d73c2a49304e16b96c67c918a9dc7c93311170d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c6ceffd401adc03dc0d619e89d73c2a49304e16b96c67c918a9dc7c93311170d","first_computed_at":"2026-05-18T00:35:50.076188Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:35:50.076188Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mhDOzhaGoyiXMHy3uyN4rNEm7cL6QCWdQF7p3EuAK5tXz580y4t03saoWQ7Oj3iiSRdVO/ot59i+W6FaFBmoAA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:35:50.076726Z","signed_message":"canonical_sha256_bytes"},"source_id":"1709.02128","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:574c13fcf659bc03e1a3fb31017a92236ecdc9cf2fc872d4949a8fc18b983fe1","sha256:48602ec9611b04d8920e36964c512fa4e06448fdcf876b9feb9e45dfb410b5cb"],"state_sha256":"3f59408bbf43fee16fa7374a697cdbee57c79b77f859657ae7221a2e8aceb286"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yQaMtcJJoLfpaA99MsEnNLaMPfqBgsf69CLAywa7VH6w0dfiiERdw0UD8Kir7NI83ipOtUvS8lXdzRY9hAxbAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-20T10:21:34.463119Z","bundle_sha256":"da3f24949446d069c3b28dc354f266580c10e4b915ff9d97fe4ced4417052961"}}