Small python implementation of the paper "Model Globally, Match Locally: Efficient and Robust 3D Object Recognition" by Drost. et al (2010) [paper].
Use the
--fastflag to use the binary extension (see instructions below)
$ python3 ppf.py --help
usage: ppf.py [-h] [--fast] [--scene-pts-fraction SCENE_PTS_FRACTION] [--ppf-num-angles PPF_NUM_ANGLES] [--ppf-rel-dist-step PPF_REL_DIST_STEP] [--alpha-num-angles ALPHA_NUM_ANGLES]
[--cluster-max-angle CLUSTER_MAX_ANGLE]
model scene
This script computes the point-pair features of a given
model and tries to find the model in a given scene.
Note: Currently, trimesh doesn't support pointcloud with normals. To combat this, you need to
reconstruct some surface between the points (e.g. ball pivoting)
positional arguments:
model Path to the model pointcloud
scene Path to the scene pointcloud
options:
-h, --help show this help message and exit
--fast Use the c++ extension for speeeeeed
--scene-pts-fraction SCENE_PTS_FRACTION
Fraction of scene points to use as reference
--ppf-num-angles PPF_NUM_ANGLES
Number of angle steps used to discretize feature angles.
--ppf-rel-dist-step PPF_REL_DIST_STEP
Discretization step of feature distances, relative to model diameter.
--alpha-num-angles ALPHA_NUM_ANGLES
Number of angle steps used to discretize the rotation angle alpha.
--cluster-max-angle CLUSTER_MAX_ANGLE
Maximal angle between poses after which they don't belong to same cluster anymore. [degrees]
Another neat thing you can do with PPF-Voting is figuring out object symmetries. You simply give the mesh of your model (or a resampled version of it) to
ppf.pyand it will figure out the rest. You'd probably want to combine it with ICP though.
- This is very much an exploratory implementation to understand the paper. No guarantees for correctness, no speed.
- As
trimeshcurrently doesn't support point clouds with normals, input need to be meshes - Currently, the code does no resampling of the input meshes, so preprocess them
- There are probably some mistakes still in the code, I discover more every now and then
To get much faster processing times, compile the binary extension. To do so, make sure you have Eigen installed on your system, a C++ compiler, CMake, etc. Then:
# to load nanobind and its dependencies
git submodule update --init --recursive
mkdir build
cd build
cmake ..
make
Additionally, symlink the resulting .so to the main folder. Now you should be able to
run ppf.py with the --fast option.
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