Point Cloud to Mesh Reconstruction: Methods, Trade-offs, and Implementation Guide
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908745221537792 |
|---|---|
| author | Iguenfer, Fatima Zahra Hsain, Achraf Amissa, Hiba Chtouki, Yousra |
| author_facet | Iguenfer, Fatima Zahra Hsain, Achraf Amissa, Hiba Chtouki, Yousra |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_10977 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Point Cloud to Mesh Reconstruction: Methods, Trade-offs, and Implementation Guide Iguenfer, Fatima Zahra Hsain, Achraf Amissa, Hiba Chtouki, Yousra Computer Vision and Pattern Recognition Graphics 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. |
| title | Point Cloud to Mesh Reconstruction: Methods, Trade-offs, and Implementation Guide |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2412.10977 |