Model structure, model training method, monomer method, equipment and medium
The invention discloses a model structure, a model training method, a monomer method, equipment and a medium. The model training method comprises the following steps: acquiring original three-dimensional point cloud data of a large-scene ground feature; making the original three-dimensional point cl...
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creator | HE WEI TAN KECHENG XU QIANGHONG LIU HAO LIU CHENGZHAO |
description | The invention discloses a model structure, a model training method, a monomer method, equipment and a medium. The model training method comprises the following steps: acquiring original three-dimensional point cloud data of a large-scene ground feature; making the original three-dimensional point cloud data into a standard sample format file; preprocessing the point cloud sample in the standard sample format file to generate a PKL format sample file; the method comprises the following steps: constructing a large-scene ground feature monomerization model, wherein the large-scene ground feature monomerization model comprises a coding module, a backbone network, a target generation module, a feature fusion module, a Point-RoIAlign module and an instance prediction network; and training the large-scale scene ground feature monomerization model by using the point cloud sample in the PKL format sample file to obtain a trained large-scale scene ground feature monomerization model. According to the method, prediction |
format | Patent |
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The model training method comprises the following steps: acquiring original three-dimensional point cloud data of a large-scene ground feature; making the original three-dimensional point cloud data into a standard sample format file; preprocessing the point cloud sample in the standard sample format file to generate a PKL format sample file; the method comprises the following steps: constructing a large-scene ground feature monomerization model, wherein the large-scene ground feature monomerization model comprises a coding module, a backbone network, a target generation module, a feature fusion module, a Point-RoIAlign module and an instance prediction network; and training the large-scale scene ground feature monomerization model by using the point cloud sample in the PKL format sample file to obtain a trained large-scale scene ground feature monomerization model. According to the method, prediction</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; HANDLING RECORD CARRIERS ; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ; PHYSICS ; PRESENTATION OF DATA ; RECOGNITION OF DATA ; RECORD CARRIERS</subject><creationdate>2022</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20220809&DB=EPODOC&CC=CN&NR=114882224A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20220809&DB=EPODOC&CC=CN&NR=114882224A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>HE WEI</creatorcontrib><creatorcontrib>TAN KECHENG</creatorcontrib><creatorcontrib>XU QIANGHONG</creatorcontrib><creatorcontrib>LIU HAO</creatorcontrib><creatorcontrib>LIU CHENGZHAO</creatorcontrib><title>Model structure, model training method, monomer method, equipment and medium</title><description>The invention discloses a model structure, a model training method, a monomer method, equipment and a medium. The model training method comprises the following steps: acquiring original three-dimensional point cloud data of a large-scene ground feature; making the original three-dimensional point cloud data into a standard sample format file; preprocessing the point cloud sample in the standard sample format file to generate a PKL format sample file; the method comprises the following steps: constructing a large-scene ground feature monomerization model, wherein the large-scene ground feature monomerization model comprises a coding module, a backbone network, a target generation module, a feature fusion module, a Point-RoIAlign module and an instance prediction network; and training the large-scale scene ground feature monomerization model by using the point cloud sample in the PKL format sample file to obtain a trained large-scale scene ground feature monomerization model. According to the method, prediction</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>HANDLING RECORD CARRIERS</subject><subject>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</subject><subject>PHYSICS</subject><subject>PRESENTATION OF DATA</subject><subject>RECOGNITION OF DATA</subject><subject>RECORD CARRIERS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2022</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZPDxzU9JzVEoLikqTS4pLUrVUcgFC5QUJWbmZealK-SmlmTkp4CE8_JzU4vg_NTC0syC3NS8EoXEvBSgaEpmaS4PA2taYk5xKi-U5mZQdHMNcfbQTS3Ij08tLkhMTs1LLYl39jM0NLGwMDIyMnE0JkYNAJDsNbE</recordid><startdate>20220809</startdate><enddate>20220809</enddate><creator>HE WEI</creator><creator>TAN KECHENG</creator><creator>XU QIANGHONG</creator><creator>LIU HAO</creator><creator>LIU CHENGZHAO</creator><scope>EVB</scope></search><sort><creationdate>20220809</creationdate><title>Model structure, model training method, monomer method, equipment and medium</title><author>HE WEI ; TAN KECHENG ; XU QIANGHONG ; LIU HAO ; LIU CHENGZHAO</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN114882224A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2022</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>HANDLING RECORD CARRIERS</topic><topic>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</topic><topic>PHYSICS</topic><topic>PRESENTATION OF DATA</topic><topic>RECOGNITION OF DATA</topic><topic>RECORD CARRIERS</topic><toplevel>online_resources</toplevel><creatorcontrib>HE WEI</creatorcontrib><creatorcontrib>TAN KECHENG</creatorcontrib><creatorcontrib>XU QIANGHONG</creatorcontrib><creatorcontrib>LIU HAO</creatorcontrib><creatorcontrib>LIU CHENGZHAO</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>HE WEI</au><au>TAN KECHENG</au><au>XU QIANGHONG</au><au>LIU HAO</au><au>LIU CHENGZHAO</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Model structure, model training method, monomer method, equipment and medium</title><date>2022-08-09</date><risdate>2022</risdate><abstract>The invention discloses a model structure, a model training method, a monomer method, equipment and a medium. The model training method comprises the following steps: acquiring original three-dimensional point cloud data of a large-scene ground feature; making the original three-dimensional point cloud data into a standard sample format file; preprocessing the point cloud sample in the standard sample format file to generate a PKL format sample file; the method comprises the following steps: constructing a large-scene ground feature monomerization model, wherein the large-scene ground feature monomerization model comprises a coding module, a backbone network, a target generation module, a feature fusion module, a Point-RoIAlign module and an instance prediction network; and training the large-scale scene ground feature monomerization model by using the point cloud sample in the PKL format sample file to obtain a trained large-scale scene ground feature monomerization model. According to the method, prediction</abstract><oa>free_for_read</oa></addata></record> |
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subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING HANDLING RECORD CARRIERS IMAGE DATA PROCESSING OR GENERATION, IN GENERAL PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS |
title | Model structure, model training method, monomer method, equipment and medium |
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