{"id":"https://openalex.org/W7128740740","doi":"https://doi.org/10.48550/arxiv.2602.10492","title":"End-to-End LiDAR optimization for 3D point cloud registration","display_name":"End-to-End LiDAR optimization for 3D point cloud registration","publication_year":2026,"publication_date":"2026-02-11","ids":{"openalex":"https://openalex.org/W7128740740","doi":"https://doi.org/10.48550/arxiv.2602.10492"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.10492","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.10492","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2602.10492","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091819282","display_name":"Siddhant Katyan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Katyan, Siddhant","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020030417","display_name":"Marc-Andr\u00e9 Gardner","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gardner, Marc-Andr\u00e9","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5125747333","display_name":"Jean-Fran\u00e7ois Lalonde","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lalonde, Jean-Fran\u00e7ois","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.7236999869346619,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.7236999869346619,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.0568000003695488,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10868","display_name":"Soft Robotics and Applications","score":0.03290000185370445,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.9132000207901001},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.7613999843597412},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.616599977016449},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4912000000476837},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.43700000643730164},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.3626999855041504},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.35089999437332153}],"concepts":[{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.9132000207901001},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.7613999843597412},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7160000205039978},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.616599977016449},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5313000082969666},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5033000111579895},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4912000000476837},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.48829999566078186},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.43700000643730164},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3626999855041504},{"id":"https://openalex.org/C166704113","wikidata":"https://www.wikidata.org/wiki/Q861092","display_name":"Image registration","level":3,"score":0.35089999437332153},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.35089999437332153},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.34470000863075256},{"id":"https://openalex.org/C163985040","wikidata":"https://www.wikidata.org/wiki/Q1172399","display_name":"Data acquisition","level":2,"score":0.3409000039100647},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.32269999384880066},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3050000071525574},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.2833999991416931},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2567000091075897},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.2515999972820282}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.10492","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.10492","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2602.10492","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.10492","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"LiDAR":[0,31,56,66,114],"sensors":[1],"are":[2,11],"a":[3],"key":[4],"modality":[5],"for":[6,42,115],"3D":[7],"perception,":[8],"yet":[9],"they":[10],"typically":[12],"designed":[13],"independently":[14],"of":[15,112],"downstream":[16],"tasks":[17],"such":[18],"as":[19],"point":[20,83],"cloud":[21],"registration.":[22],"Conventional":[23],"registration":[24,69,73,89],"operates":[25],"on":[26],"pre-acquired":[27],"datasets":[28],"with":[29],"fixed":[30],"configurations,":[32],"leading":[33],"to":[34],"suboptimal":[35],"data":[36],"collection":[37],"and":[38,46,68,86,91,118],"significant":[39],"computational":[40],"overhead":[41],"sampling,":[43],"noise":[44],"filtering,":[45],"parameter":[47],"tuning.":[48],"In":[49],"this":[50],"work,":[51],"we":[52],"propose":[53],"an":[54],"adaptive":[55,113],"sensing":[57,77],"framework":[58],"that":[59,99],"dynamically":[60],"adjusts":[61],"sensor":[62],"parameters,":[63],"jointly":[64],"optimizing":[65],"acquisition":[67],"hyperparameters.":[70],"By":[71],"integrating":[72],"feedback":[74],"into":[75],"the":[76,95,110],"loop,":[78],"our":[79,100],"approach":[80],"optimally":[81],"balances":[82],"density,":[84],"noise,":[85],"sparsity,":[87],"improving":[88],"accuracy":[90],"efficiency.":[92],"Evaluations":[93],"in":[94],"CARLA":[96],"simulation":[97],"demonstrate":[98],"method":[101],"outperforms":[102],"fixed-parameter":[103],"baselines":[104],"while":[105],"retaining":[106],"generalization":[107],"abilities,":[108],"highlighting":[109],"potential":[111],"autonomous":[116],"perception":[117],"robotic":[119],"applications.":[120]},"counts_by_year":[],"updated_date":"2026-08-16T07:02:28.622633","created_date":"2026-02-13T00:00:00"}
