150268 20251017144603.0 doi 10.1109/LRA.2020.3007440 sideral 119105 ART-2020-119105 eng Alonso, I. Universidad de Zaragoza (orcid)0000-0003-4638-4655 3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation 2020 Access copy available to the general public Unrestricted LIDAR semantic segmentation is an essential task that provides 3D semantic information about the environment to robots. Fast and efficient semantic segmentation methods are needed to match the strong computational and temporal restrictions of many real-world robotic applications. This work presents 3D-MiniNet, a novel approach for LIDAR semantic segmentation that combines 3D and 2D learning layers. It first learns a 2D representation from the raw points through a novel projection which extracts local and global information from the 3D data. This representation is fed to an efficient 2D Fully Convolutional Neural Network (FCNN) that produces a 2D semantic segmentation. These 2D semantic labels are re-projected back to the 3D space and enhanced through a post-processing module. The main novelty in our strategy relies on the projection learning module. Our detailed ablation study shows how each component contributes to the final performance of 3D-MiniNet. We validate our approach on well known public benchmarks (SemanticKITTI and KITTI), where 3D-MiniNet gets state-of-the-art results while being faster and more parameter-efficient than previous methods. info:eu-repo/grantAgreement/ES/DGA-FSE/T45-17R info:eu-repo/grantAgreement/ES/MCIU-AEI/RTC-2017-6421-7 info:eu-repo/grantAgreement/ES/MICIU-AEI-FEDER/PGC2018-098817-A-I00 info:eu-repo/grantAgreement/ES/MICIU/PID2019-105390RB-I00 info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 3.741 2020 ROBOTICS 9 / 28 = 0.321 2020 Q2 T1 1.123 2020 Artificial Intelligence 2020 Q1 Biomedical Engineering 2020 Q1 Computer Science Applications 2020 Q1 Mechanical Engineering 2020 Q1 Control and Optimization 2020 Q1 Control and Systems Engineering 2020 Q1 Human-Computer Interaction 2020 Q1 Computer Vision and Pattern Recognition 2020 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Riazuelo, L. Universidad de Zaragoza (orcid)0000-0002-6722-5541 Montesano, L. Universidad de Zaragoza (orcid)0000-0003-1183-349X Murillo, A.C. Universidad de Zaragoza (orcid)0000-0002-7580-9037 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 5, 4 (2020), 5432-5439 IEEE Robot. autom. let. IEEE Robotics and Automation Letters 2377-3766 893403 http://zaguan.unizar.es/record/150268/files/texto_completo.pdf Postprint 3214439 http://zaguan.unizar.es/record/150268/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:150268 articulos driver 2025-10-17-14:14:33 ARTICLE