{"id":"https://openalex.org/W3079291257","doi":"https://doi.org/10.1145/3388770.3407423","title":"6D Pose Estimation with Two-stream Net","display_name":"6D Pose Estimation with Two-stream Net","publication_year":2020,"publication_date":"2020-08-15","ids":{"openalex":"https://openalex.org/W3079291257","doi":"https://doi.org/10.1145/3388770.3407423","mag":"3079291257"},"language":"en","primary_location":{"id":"doi:10.1145/3388770.3407423","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3388770.3407423","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM SIGGRAPH 2020 Posters","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101546994","display_name":"Xiaolong Yang","orcid":"https://orcid.org/0009-0000-1790-2148"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Xiaolong Yang","raw_affiliation_strings":["KLMM, AMSS, CAS and Univ. of CAS"],"affiliations":[{"raw_affiliation_string":"KLMM, AMSS, CAS and Univ. of CAS","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075570681","display_name":"Xiaohong Jia","orcid":"https://orcid.org/0000-0001-6206-3216"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiaohong Jia","raw_affiliation_strings":["KLMM, AMSS, CAS and Univ. of CAS"],"affiliations":[{"raw_affiliation_string":"KLMM, AMSS, CAS and Univ. of CAS","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101546994"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1471,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.44788936,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"2"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10653","display_name":"Robot Manipulation and Learning","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9638000130653381,"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/T12549","display_name":"Image and Object Detection Techniques","score":0.9531999826431274,"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/pose","display_name":"Pose","score":0.8303424119949341},{"id":"https://openalex.org/keywords/ransac","display_name":"RANSAC","score":0.7682217359542847},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.7571699619293213},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7565388679504395},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7535130977630615},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5937150716781616},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5555948615074158},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5227028131484985},{"id":"https://openalex.org/keywords/3d-pose-estimation","display_name":"3D pose estimation","score":0.5203349590301514},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5029675364494324},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5014982223510742},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.46817344427108765},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.43763595819473267},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.41833531856536865},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2846109867095947}],"concepts":[{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.8303424119949341},{"id":"https://openalex.org/C114744707","wikidata":"https://www.wikidata.org/wiki/Q218533","display_name":"RANSAC","level":3,"score":0.7682217359542847},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.7571699619293213},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7565388679504395},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7535130977630615},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5937150716781616},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5555948615074158},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5227028131484985},{"id":"https://openalex.org/C36613465","wikidata":"https://www.wikidata.org/wiki/Q4636322","display_name":"3D pose estimation","level":3,"score":0.5203349590301514},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5029675364494324},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5014982223510742},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.46817344427108765},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43763595819473267},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.41833531856536865},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2846109867095947},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3388770.3407423","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3388770.3407423","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM SIGGRAPH 2020 Posters","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.8199999928474426,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1855641990","https://openalex.org/W2920961587","https://openalex.org/W2950921159","https://openalex.org/W2963177347","https://openalex.org/W2963188159","https://openalex.org/W2981623153","https://openalex.org/W3100052745","https://openalex.org/W3102636071","https://openalex.org/W4300828864"],"related_works":["https://openalex.org/W2896146757","https://openalex.org/W4253893311","https://openalex.org/W3089306886","https://openalex.org/W2113785214","https://openalex.org/W2798721181","https://openalex.org/W3201205132","https://openalex.org/W1018308721","https://openalex.org/W4229588126","https://openalex.org/W4390872624","https://openalex.org/W4229059082"],"abstract_inverted_index":{"In":[0],"this":[1],"poster,":[2],"we":[3,17,41,74],"present":[4],"a":[5,19],"heterogeneous":[6],"architecture":[7],"for":[8],"estimating":[9],"6D":[10],"object":[11],"pose":[12,63,79,87],"from":[13],"RGB":[14,34],"images.":[15],"First,":[16],"use":[18],"two-stream":[20],"network":[21,45],"to":[22,46],"extract":[23],"robust":[24],"3D-to-2D":[25],"embedding":[26],"feature":[27],"correspondence.":[28],"The":[29,62],"segmentation":[30],"stream":[31],"processes":[32],"the":[33,54,59,85],"information":[35],"and":[36,49,52],"spatial":[37],"features":[38],"individually.":[39],"Then,":[40],"construct":[42],"another":[43],"fusion":[44],"couple":[47],"color":[48],"positional":[50],"features,":[51],"predict":[53],"locations":[55],"of":[56],"keypoints":[57],"in":[58,94],"regression":[60],"stream.":[61],"can":[64],"be":[65],"obtained":[66],"by":[67],"an":[68,76],"efficient":[69],"RANSAC-based":[70],"PnP":[71],"algorithm.":[72],"Moreover,":[73],"design":[75],"end-to-end":[77],"iterative":[78],"refinement":[80],"procedure":[81],"that":[82],"further":[83],"improves":[84],"reliable":[86],"estimation.":[88],"Our":[89],"method":[90],"outperforms":[91],"state-of-the-art":[92],"approaches":[93],"two":[95],"public":[96],"datasets.":[97]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
