{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:LZGNR2XYBPAKXFSQOUTE77IXFF","short_pith_number":"pith:LZGNR2XY","schema_version":"1.0","canonical_sha256":"5e4cd8eaf80bc0ab965075264ffd17296daa680c819c51607db5fbd5f95e99bc","source":{"kind":"arxiv","id":"1902.07830","version":4},"attestation_state":"computed","paper":{"title":"Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Christian Haase-Sch\\\"utz, Claudius Glaeser, Di Feng, Fabian Timm, Heinz Hertlein, Klaus Dietmayer, Lars Rosenbaum, Werner Wiesbeck","submitted_at":"2019-02-21T01:11:51Z","abstract_excerpt":"Recent advancements in perception for autonomous driving are driven by deep learning. In order to achieve robust and accurate scene understanding, autonomous vehicles are usually equipped with different sensors (e.g. cameras, LiDARs, Radars), and multiple sensing modalities can be fused to exploit their complementary properties. In this context, many methods have been proposed for deep multi-modal perception problems. However, there is no general guideline for network architecture design, and questions of \"what to fuse\", \"when to fuse\", and \"how to fuse\" remain open. This review paper attempts"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1902.07830","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-02-21T01:11:51Z","cross_cats_sorted":[],"title_canon_sha256":"dd033634d79297657df415af8493483e98ca8b77faede73b065e10d778ce626e","abstract_canon_sha256":"eff9e8be327bd65801d79c045fd4ecf4bb7a3d098d8fea54ad0bc0823b1a8fd4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:19:49.446907Z","signature_b64":"nl4K0ncKx/rpKPXoxOQojQLld/Z+YhKuWLoy61dG82zqbFO2Ooyw49jtBgRaWXmbgnxN0a6oXc/7mTkmloicDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5e4cd8eaf80bc0ab965075264ffd17296daa680c819c51607db5fbd5f95e99bc","last_reissued_at":"2026-07-05T01:19:49.446368Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:19:49.446368Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Christian Haase-Sch\\\"utz, Claudius Glaeser, Di Feng, Fabian Timm, Heinz Hertlein, Klaus Dietmayer, Lars Rosenbaum, Werner Wiesbeck","submitted_at":"2019-02-21T01:11:51Z","abstract_excerpt":"Recent advancements in perception for autonomous driving are driven by deep learning. In order to achieve robust and accurate scene understanding, autonomous vehicles are usually equipped with different sensors (e.g. cameras, LiDARs, Radars), and multiple sensing modalities can be fused to exploit their complementary properties. In this context, many methods have been proposed for deep multi-modal perception problems. However, there is no general guideline for network architecture design, and questions of \"what to fuse\", \"when to fuse\", and \"how to fuse\" remain open. This review paper attempts"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.07830","kind":"arxiv","version":4},"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/1902.07830/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1902.07830","created_at":"2026-07-05T01:19:49.446435+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.07830v4","created_at":"2026-07-05T01:19:49.446435+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.07830","created_at":"2026-07-05T01:19:49.446435+00:00"},{"alias_kind":"pith_short_12","alias_value":"LZGNR2XYBPAK","created_at":"2026-07-05T01:19:49.446435+00:00"},{"alias_kind":"pith_short_16","alias_value":"LZGNR2XYBPAKXFSQ","created_at":"2026-07-05T01:19:49.446435+00:00"},{"alias_kind":"pith_short_8","alias_value":"LZGNR2XY","created_at":"2026-07-05T01:19:49.446435+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"1907.02149","citing_title":"Analyzing the Cross-Sensor Portability of Neural Network Architectures for LiDAR-based Semantic Labeling","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2605.16397","citing_title":"Trajectory-Aware Adaptive Inference in Object Detection Models","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"1903.11027","citing_title":"nuScenes: A multimodal dataset for autonomous driving","ref_index":29,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LZGNR2XYBPAKXFSQOUTE77IXFF","json":"https://pith.science/pith/LZGNR2XYBPAKXFSQOUTE77IXFF.json","graph_json":"https://pith.science/api/pith-number/LZGNR2XYBPAKXFSQOUTE77IXFF/graph.json","events_json":"https://pith.science/api/pith-number/LZGNR2XYBPAKXFSQOUTE77IXFF/events.json","paper":"https://pith.science/paper/LZGNR2XY"},"agent_actions":{"view_html":"https://pith.science/pith/LZGNR2XYBPAKXFSQOUTE77IXFF","download_json":"https://pith.science/pith/LZGNR2XYBPAKXFSQOUTE77IXFF.json","view_paper":"https://pith.science/paper/LZGNR2XY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.07830&json=true","fetch_graph":"https://pith.science/api/pith-number/LZGNR2XYBPAKXFSQOUTE77IXFF/graph.json","fetch_events":"https://pith.science/api/pith-number/LZGNR2XYBPAKXFSQOUTE77IXFF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LZGNR2XYBPAKXFSQOUTE77IXFF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LZGNR2XYBPAKXFSQOUTE77IXFF/action/storage_attestation","attest_author":"https://pith.science/pith/LZGNR2XYBPAKXFSQOUTE77IXFF/action/author_attestation","sign_citation":"https://pith.science/pith/LZGNR2XYBPAKXFSQOUTE77IXFF/action/citation_signature","submit_replication":"https://pith.science/pith/LZGNR2XYBPAKXFSQOUTE77IXFF/action/replication_record"}},"created_at":"2026-07-05T01:19:49.446435+00:00","updated_at":"2026-07-05T01:19:49.446435+00:00"}