2024/12/14 by Fatima Zahra Iguenfer, Achraf Hsain, Iguenfer, Fatima Zahra +5
Earth and Planetary Sciences · Engineering · Environmental Science · #3D Shape Modeling and Analysis #3D Surveying and Cultural Heritage #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Graphics (cs.GR) #Remote Sensing and LiDAR Applications
paper · pdf · doi:10.48550/arxiv.2412.10977
openalex publication_date 2024/12/14 · openalex created_date 2024/12/18 · openalex updated_date 2026/07/28
Reconstructing meshes from point clouds is a fundamental task in computer vision with applications spanning robotics, autonomous systems, and medical imaging. Selecting an appropriate learning-based method requires understanding trade-offs between computational efficiency, geometric accuracy, and output constraints. This paper categorizes over fifteen methods into five paradigms -- PointNet family, autoencoder architectures, deformation-based methods, point-move techniques, and primitive-based approaches -- and provides practical guidance for method selection. We contribute: (1) a decision framework mapping input/output requirements to suitable paradigms, (2) a failure mode analysis to assist practitioners in debugging implementations, (3) standardized comparisons on ShapeNet benchmarks, and (4) a curated list of maintained codebases with implementation resources. By synthesizing both theoretical foundations and practical considerations, this work serves as an entry point for practitioners and researchers new to learning-based 3D mesh reconstruction.