{"id":"https://openalex.org/W4377371825","doi":"https://doi.org/10.48550/arxiv.2305.11601","title":"Towards Better Gradient Consistency for Neural Signed Distance Functions via Level Set Alignment","display_name":"Towards Better Gradient Consistency for Neural Signed Distance Functions via Level Set Alignment","publication_year":2023,"publication_date":"2023-05-19","ids":{"openalex":"https://openalex.org/W4377371825","doi":"https://doi.org/10.48550/arxiv.2305.11601"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2305.11601","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.11601","pdf_url":"https://arxiv.org/pdf/2305.11601","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2305.11601","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034970671","display_name":"Baorui Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Baorui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060588302","display_name":"Junsheng Zhou","orcid":"https://orcid.org/0000-0002-1919-8227"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Junsheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101691399","display_name":"Yu-Shen Liu","orcid":"https://orcid.org/0000-0001-7305-1915"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yu-Shen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5068597652","display_name":"Zhizhong Han","orcid":"https://orcid.org/0000-0001-9540-9973"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Zhizhong","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":2,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9884999990463257,"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"}},"topics":[{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9884999990463257,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9879999756813049,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9764000177383423,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/signed-distance-function","display_name":"Signed distance function","score":0.710612416267395},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.6997886300086975},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.6329317688941956},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6037157773971558},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5893865823745728},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.544716477394104},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5170767307281494},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4948810935020447},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.49204227328300476},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4432172477245331},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41087839007377625},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3316243290901184},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3141791820526123},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.16297030448913574}],"concepts":[{"id":"https://openalex.org/C71169176","wikidata":"https://www.wikidata.org/wiki/Q7512907","display_name":"Signed distance function","level":2,"score":0.710612416267395},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.6997886300086975},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.6329317688941956},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6037157773971558},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5893865823745728},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.544716477394104},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5170767307281494},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4948810935020447},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.49204227328300476},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4432172477245331},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41087839007377625},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3316243290901184},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3141791820526123},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.16297030448913574},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2305.11601","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.11601","pdf_url":"https://arxiv.org/pdf/2305.11601","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2305.11601","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2305.11601","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"pmh:oai:arXiv.org:2305.11601","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.11601","pdf_url":"https://arxiv.org/pdf/2305.11601","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4377371825.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W169516377","https://openalex.org/W2164905646","https://openalex.org/W2898479565","https://openalex.org/W2377107237","https://openalex.org/W3016928466","https://openalex.org/W4389574804","https://openalex.org/W2936725271","https://openalex.org/W3150655618","https://openalex.org/W4390189952","https://openalex.org/W4379473983"],"abstract_inverted_index":{"Neural":[0],"signed":[1,16],"distance":[2,17],"functions":[3],"(SDFs)":[4],"have":[5],"shown":[6],"remarkable":[7],"capability":[8],"in":[9,44,89,115,130,139],"representing":[10],"geometry":[11],"with":[12],"details.":[13],"However,":[14],"without":[15],"supervision,":[18],"it":[19],"is":[20,54,121,143,163],"still":[21],"a":[22,65,164],"challenge":[23],"to":[24,70,81,97,122,128,136,174],"infer":[25,175],"SDFs":[26,176,199],"from":[27,157,177,201],"point":[28,150,179,202],"clouds":[29,151,180,203],"or":[30,152,204],"multi-view":[31,158,182,205],"images":[32,206],"using":[33],"neural":[34],"networks.":[35],"In":[36],"this":[37],"paper,":[38],"we":[39,63,91],"claim":[40],"that":[41,90,142,190],"gradient":[42,84],"consistency":[43],"the":[45,49,55,59,72,98,111,124,131,140,146,153,196],"field,":[46],"indicated":[47],"by":[48,102,145],"parallelism":[50,73],"of":[51,74,148,155,198],"level":[52,66,75,95,100,113,126],"sets,":[53,76],"key":[56],"factor":[57],"affecting":[58],"inference":[60],"accuracy.":[61],"Hence,":[62],"propose":[64],"set":[67,101,114,127],"alignment":[68],"loss":[69,162,192],"evaluate":[71],"which":[77,167],"can":[78,92,168,193],"be":[79,169],"minimized":[80],"achieve":[82],"better":[83],"consistency.":[85],"Our":[86,119,160,184],"novelty":[87],"lies":[88],"align":[93],"all":[94],"sets":[96],"zero":[99,112,125],"constraining":[103],"gradients":[104,135],"at":[105,215],"queries":[106],"and":[107,181,186,211],"their":[108],"projections":[109],"on":[110],"an":[116],"adaptive":[117],"way.":[118],"insight":[120],"propagate":[123],"everywhere":[129],"field":[132,141],"through":[133],"consistent":[134],"eliminate":[137],"uncertainty":[138],"caused":[144],"discreteness":[147],"3D":[149,178],"lack":[154],"observations":[156],"images.":[159,183],"proposed":[161],"general":[165],"term":[166],"used":[170],"upon":[171],"different":[172],"methods":[173],"numerical":[185],"visual":[187],"comparisons":[188],"demonstrate":[189],"our":[191],"significantly":[194],"improve":[195],"accuracy":[197],"inferred":[200],"under":[207],"various":[208],"benchmarks.":[209],"Code":[210],"data":[212],"are":[213],"available":[214],"https://github.com/mabaorui/TowardsBetterGradient":[216],".":[217]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}
