framesh 0.3

Local reference frame algorithms for meshes

🖼️ Framesh

A collection of robust Local Reference Frame (LRF) algorithms for 3D mesh patches.

Overview

Framesh provides efficient implementations of several state-of-the-art Local Reference Frame computation methods for 3D meshes. LRFs are essential for creating rotation-invariant local surface descriptors and establishing repeatable coordinate systems on 3D surfaces. The library has minimal dependencies, requiring only NumPy for computation and Trimesh for mesh handling.

Installation

Framesh is available on PyPI and can be installed using pip:

python -m pip install framesh

Usage

Each LRF method follows a consistent API:

import trimesh
from framesh import shot_lrf

# Load your mesh
mesh = trimesh.load('your_mesh.obj')
# Compute LRF for vertex index 0 with default parameters
lrf = shot_lrf(mesh, vertex_index=0, radius=1.0)
# The result is a 3x3 matrix where columns are [x-axis, y-axis, z-axis]
print(lrf)

Each method also comes with a vectorized version for computing LRFs on multiple vertices simultaneously, offering better performance for batch processing:

import trimesh
import numpy as np
from framesh import shot_frames

# Load your mesh
mesh = trimesh.load('your_mesh.obj')
# Compute LRFs for multiple vertices
vertex_indices = np.array([0, 1, 2, 3])  # compute LRFs for first 4 vertices
lrfs = shot_frames(mesh, vertex_indices=vertex_indices, radius=1.0)
# The result is a batch of 3x3 matrices with shape (n_vertices, 3, 3)
print(lrfs.shape)  # outputs: (4, 3, 3)

Implemented Methods

• SHOT

  • Implementation of the LRF computation from the SHOT (Signature of Histograms of OrienTations) descriptor. Uses weighted covariance analysis to establish a robust coordinate system.
  • Reference: Tombari et al., "Unique signatures of histograms for local surface description." ECCV 2010.

• BOARD

  • Board method for LRF computation. Creates a robust coordinate system using plane fitting and normal-based point selection strategies.
  • Reference: Petrelli & Di Stefano, "On the repeatability of the local reference frame for partial shape matching." ICCV 2011.

• FLARE

  • Fast Local Axis Reference Extraction method. Combines plane fitting for z-axis computation with a distance-based point selection strategy for x-axis determination.
  • Reference: Petrelli & Di Stefano, "A Repeatable and Efficient Canonical Reference for Surface Matching." 3DIMPVT 2012.

• ROPS

  • Rotational Projection Statistics-based LRF computation. Utilizes face areas and distances to construct a weighted scatter matrix for axis determination.
  • Reference: Guo et al., "A local feature descriptor for 3D rigid objects based on rotational projection statistics." ICCSPA 2013.

• TOLDI

  • Triangular-based Overlapping Local Depth Images LRF computation. Uses a combination of PCA and projection-based weighting for robust axis determination.
  • Reference: Yang et al., "TOLDI: An effective and robust approach for 3D local shape description." Pattern Recognition 2017.

• GFrames

  • Gradient-based local reference frame computation. Utilizes the gradient of a scalar field defined on the mesh surface to determine axis directions.
  • Reference: Melzi et al., "GFrames: Gradient-based local reference frame for 3D shape matching." CVPR 2019.

Citation

If you use this code in your research, please cite:

@software{grementieri2024framesh,
    author = {Grementieri, Luca},
    title = {Framesh: A Collection of Local Reference Frame Algorithms for 3D Mesh Patches},
    year = {2024},
    publisher = {GitHub},
    url = {https://github.com/lucagrementieri/framesh},
    note = {A library implementing multiple classical Local Reference Frame (LRF) algorithms}
}