{"687232":{"#nid":"687232","#data":{"type":"event","title":"PhD Proposal by Andrew Yarovoi","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E Generalized 3D Point Cloud Understanding via Cross-Domain Multi-Dataset Learning.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EDate:\u003C\/strong\u003E\u0026nbsp;January 22, 2026\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETime:\u0026nbsp;\u003C\/strong\u003E1pm EST\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ELocation: \u003C\/strong\u003EVan Leer 218\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EVirtual Link:\u003C\/strong\u003E\u0026nbsp;\u003Ca href=\u0022https:\/\/gatech.zoom.us\/j\/96837308246\u0022 title=\u0022https:\/\/gatech.zoom.us\/j\/96837308246\u0022\u003EZoom\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003EMeeting ID: 968 3730 8246\u003C\/p\u003E\u003Cp\u003EPasscode: 650576\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAndrew Yarovoi\u003C\/strong\u003E\u003Cbr\u003EPh.D. Student, Robotics\u003Cbr\u003EGeorge W. Woodruff School of Mechanical Engineering\u003Cbr\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EDr. Christopher R. Valenta (Advisor)\u003C\/p\u003E\u003Cp\u003ESchool of Electrical and Computer Engineering\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDr. Lu Gan\u003C\/p\u003E\u003Cp\u003EGuggenheim School of Aerospace Engineering\u003C\/p\u003E\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDr. James Hays\u0026nbsp;\u003C\/p\u003E\u003Cp\u003ESchool of Interactive Computing\u003C\/p\u003E\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDr. Zsolt Kira\u003C\/p\u003E\u003Cp\u003ESchool of Interactive Computing\u003C\/p\u003E\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDr. Patricio A. Vela\u003C\/p\u003E\u003Cp\u003ESchool of Electrical and Computer Engineering\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract: \u0026nbsp;\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EThe proposed research aims to advance 3D point cloud semantic understand by enabling effective multi-dataset training (MDT) across diverse domains and sensors. Current models trained on individual datasets often exhibit limited generalization due to restricted point cloud dataset sizes and sensor-specific biases. MDT offers a solution by providing more diverse training data from a variety of sensors and domains. However, implementing MDT for point cloud data remains challenging due to unresolved issues such as heterogeneous input features, insufficient contextual information, significant variations in scale and density, and inconsistent labeling across datasets. The proposed work aims to systematically address these challenges through architectural design. First, a modular training framework will be developed to accommodate heterogeneous sensor inputs and align synonymous class labels through an open-vocabulary approach. Second, novel expansion and contraction network blocks will be designed to efficiently generate multi-resolution features, enhancing scale and density invariance. Third, a new backbone model, termed UniMD3D, will integrate these modules and implement a unified label alignment mechanism to resolve class partitioning inconsistencies across datasets. The proposed research is expected to enable effective learning while training on mixed datasets spanning multiple domains, tasks, and sensors, leading to more generalized 3D perception systems for real-world applications.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EGeneralized 3D Point Cloud Understanding via Cross-Domain Multi-Dataset Learning\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Generalized 3D Point Cloud Understanding via Cross-Domain Multi-Dataset Learning"}],"uid":"27707","created_gmt":"2026-01-13 13:49:13","changed_gmt":"2026-01-13 13:50:23","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-01-22T13:00:00-05:00","event_time_end":"2026-01-22T15:00:00-05:00","event_time_end_last":"2026-01-22T15:00:00-05:00","gmt_time_start":"2026-01-22 18:00:00","gmt_time_end":"2026-01-22 20:00:00","gmt_time_end_last":"2026-01-22 20:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Van Leer 218","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"102851","name":"Phd proposal"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}