<Record><identifier xmlns="http://purl.org/dc/elements/1.1/">URN:NBN:SI:DOC-6EYQUPFF</identifier><date>2025</date><creator>Remmach, Hassnae</creator><relation>documents/doc/6/URN_NBN_SI_doc-6EYQUPFF_001.pdf</relation><relation>documents/doc/6/URN_NBN_SI_doc-6EYQUPFF_001.txt</relation><format format_type="volume">49</format><format format_type="issue">5</format><format format_type="type">article</format><format format_type="extent">str. 37-48</format><identifier identifier_type="DOI">10.31449/inf.v49i5.6863</identifier><identifier identifier_type="ISSN">1854-3871</identifier><identifier identifier_type="COBISSID">231379715</identifier><identifier identifier_type="URN">URN:NBN:SI:doc-6EYQUPFF</identifier><language>eng</language><publisher publisher_location="Ljubljana">Informatika</publisher><source>Informatica (Ljubljana)</source><rights>BY</rights><subject language_type_id="slv">3d rekonstrukcija</subject><subject language_type_id="slv">konvolucijske nevronske mreže</subject><subject language_type_id="slv">oblak točk</subject><subject language_type_id="slv">umetna inteligenca</subject><subject language_type_id="slv">večizhodni regresor</subject><subject language_type_id="slv">večopravilni regresor</subject><title>CNN-based multi-output and multi-task regression for supershape reconstruction from 3D point clouds</title></Record>