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Specifically, we present a novel data-driven method that learns features that can be used to align clean CAD models from a workpiece database to the noisy and incomplete geometry of a RGBD scan. Using the learned features with Random sample consensus (RANSAC) for CAD to scan registration, learned features improve registration result as compared to traditional approaches by (translation error (<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\Delta $$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mi>\u0394<\/mml:mi>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>18.47 mm) and rotation error(<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\Delta 43 ^\\circ $$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mi>\u0394<\/mml:mi>\n                    <mml:msup>\n                      <mml:mn>43<\/mml:mn>\n                      <mml:mo>\u2218<\/mml:mo>\n                    <\/mml:msup>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>)) and accuracy(35%) respectively. Furthermore, a 3D-vision based automatic burr detection and height estimation technique is presented. The estimated burr heights were verified and compared with measurements from a high resolution industrial CT scanning machine. Together with registration, our burr height estimation approach is able to estimate burr height similar to high resolution CT scans with Z-statistic value (<jats:inline-formula><jats:alternatives><jats:tex-math>$$z=0.279$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mi>z<\/mml:mi>\n                    <mml:mo>=<\/mml:mo>\n                    <mml:mn>0.279<\/mml:mn>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>).<\/jats:p>","DOI":"10.1007\/s10845-022-02036-6","type":"journal-article","created":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T03:37:54Z","timestamp":1665459474000},"page":"303-314","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Automated 3D burr detection in cast manufacturing using sparse convolutional neural networks"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3074-3000","authenticated-orcid":false,"given":"Ahmed","family":"Mohammed","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes","family":"Kvam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0839-0221","authenticated-orcid":false,"given":"Ingrid Fjordheim","family":"Onstein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marianne","family":"Bakken","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Helene","family":"Schulerud","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,11]]},"reference":[{"issue":"2","key":"2036_CR1","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1016\/j.cirp.2009.09.004","volume":"58","author":"JC Aurich","year":"2009","unstructured":"Aurich, J. 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