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.
@article{arxiv.2412.10977,
title = {Point Cloud to Mesh Reconstruction: Methods, Trade-offs, and Implementation Guide},
author = {Fatima Zahra Iguenfer and Achraf Hsain and Hiba Amissa and Yousra Chtouki},
journal= {arXiv preprint arXiv:2412.10977},
year = {2026}
}