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This study proposes a robust method for detecting handle\u2010like regions in common objects, focusing on slender handles distinct from the main body. This characteristic is prevalent in many daily\u2010use objects that are often manipulated. Our method employs the scale\u2010invariant heat kernel signature (SI\u2010HKS) descriptor to capture local and global shape features of 3D objects. By utilizing SI\u2010HKS properties, we extract meaningful geometric information. Points are classified into segments using the XGBoost classifier, known for its efficiency and accuracy, while hyperparameters are optimized through random search. A post\u2010processing step refines handle detection by filtering out non\u2010graspable regions based on geometric skeleton curvature. The proposed approach is evaluated on a custom dataset in two configurations: five categories of handle\u2010equipped objects and extended version with eleven categories. In the 5\u2010class setup, the method achieves a mean intersection\u2010over\u2010union (mIoU) of 97.6%, outperforming leading deep learning models like PointNet, PointNet++, and DGCNN with statistically significant improvements confirmed by\n                    <jats:italic>t<\/jats:italic>\n                    \u2010tests. In the extended 11\u2010class setup, the method maintains a strong performance with a mean IoU of 97.5%. The use of intrinsic geometric features enhances rotation invariance, ensuring consistent segmentation across different orientations.\n                  <\/jats:p>","DOI":"10.1049\/ipr2.70225","type":"journal-article","created":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T07:56:01Z","timestamp":1759737361000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Object Handle Segmentation in 3D Point Cloud for Robot Grasping Using Scale Invariant Heat Kernel Signature With Optimized XGBoost Classifier"],"prefix":"10.1049","volume":"19","author":[{"given":"Haniye","family":"Merrikhi","sequence":"first","affiliation":[{"name":"Computer Vision Research Laboratory Faculty of Electrical and Computer Engineering Sahand University of Technology  Tabriz Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4071-2750","authenticated-orcid":false,"given":"Hossein","family":"Ebrahimnezhad","sequence":"additional","affiliation":[{"name":"Computer Vision Research Laboratory Faculty of Electrical and Computer Engineering Sahand University of Technology  Tabriz Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"265","published-online":{"date-parts":[[2025,10,6]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042\u2010019\u201008302\u20109"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105694"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2011.07.022"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/S11431\u2010022\u20102182\u2010Y\/METRICS"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10514\u2010018\u20109784\u20108"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.COMPAG.2020.105818"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462\u2010020\u201009888\u20105"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2018.2852777"},{"key":"e_1_2_9_10_1","doi-asserted-by":"crossref","unstructured":"S.AinetterandF.Fraundorfer \u201cEnd\u2010to\u2010End Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation From RGB \u201d inProceedings of IEEE International Conference on Robotics and Automation(IEEE 2021) 13452\u201313458 https:\/\/doi.org\/10.1109\/ICRA48506.2021.9561398.","DOI":"10.1109\/ICRA48506.2021.9561398"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s43154\u2010020\u201000021\u20106"},{"key":"e_1_2_9_12_1","doi-asserted-by":"crossref","unstructured":"S.Kumra S.Joshi andF.Sahin \u201cAntipodal Robotic Grasping Using Generative Residual Convolutional Neural Network \u201d inIEEE\/RSJ International Conference on Intelligent Robots and Systems(IEEE 2020) 9626\u20139633 https:\/\/doi.org\/10.1109\/IROS45743.2020.9340777.","DOI":"10.1109\/IROS45743.2020.9340777"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10846\u2010020\u201001202\u20103"},{"key":"e_1_2_9_14_1","doi-asserted-by":"crossref","unstructured":"M.Gou H. 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