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Point Cloud to Mesh Reconstruction: Methods, Trade-offs, and Implementation Guide

Fatima Zahra Iguenfer, Achraf Hsain, Hiba Amissa, Yousra Chtouki

TL;DR

This paper addresses the challenge of reconstructing 3D meshes from point clouds by organizing learning-based approaches into five paradigms: PointNet family, autoencoder architectures, deformation-based methods, point-move techniques, and primitive-based strategies. It provides a practical method-selection framework based on input characteristics and output requirements, complemented by a failure-mode analysis, standardized ShapeNet/ModelNet comparisons, and curated code resources. Key contributions include a decision framework for paradigm selection, guidance on debugging and evaluation, and consolidated repositories and datasets to accelerate adoption. The work significantly aids practitioners and researchers by unifying theory and practice, offering actionable guidance for real-world mesh reconstruction tasks across diverse applications.

Abstract

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.

Point Cloud to Mesh Reconstruction: Methods, Trade-offs, and Implementation Guide

TL;DR

This paper addresses the challenge of reconstructing 3D meshes from point clouds by organizing learning-based approaches into five paradigms: PointNet family, autoencoder architectures, deformation-based methods, point-move techniques, and primitive-based strategies. It provides a practical method-selection framework based on input characteristics and output requirements, complemented by a failure-mode analysis, standardized ShapeNet/ModelNet comparisons, and curated code resources. Key contributions include a decision framework for paradigm selection, guidance on debugging and evaluation, and consolidated repositories and datasets to accelerate adoption. The work significantly aids practitioners and researchers by unifying theory and practice, offering actionable guidance for real-world mesh reconstruction tasks across diverse applications.

Abstract

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.

Paper Structure

This paper contains 41 sections, 5 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Method Selection Framework
  3. Input Characteristics
  4. Output Requirements
  5. Decision Guidelines
  6. The PointNet Family
  7. PointNet
  8. PointTriNet: Learned Triangulation of 3D Point Sets
  9. PCN: Point Completion Network
  10. Autoencoder Architectures
  11. AtlasNet
  12. 3D Mesh Generation from a Defective Point Cloud using Style Transformation
  13. Deformation-based Methods
  14. Isomorphic Mesh Generation From Point Clouds With MLPs
  15. Meshing Point Clouds with Predicted Intrinsic-Extrinsic Ratio Guidance
  16. ...and 26 more sections

Figures (5)

  • Figure 1: PointNet Architecture qi2017pointnet
  • Figure 2: PCN Architecture yuan2018pcn
  • Figure 3: Fast Point Cloud to Mesh Reconstruction for Deformable Object Tracking Pipeline mansour2023
  • Figure 4: Point Cloud to Mesh using Regression Pipeline ladicky2017
  • Figure 5: Sharp Feature-Preserving 3D Mesh Reconstruction Pipeline liu2023sharp