Lumps: Shape Model directly from Mesh

What and Where is the Use Case?

This use case demonstrates a minimal example to run ShapeWorks directly on a mesh using a synthetic dataset.

The use case is located at: Examples/Python/

Lumps Dataset

Running the Use Case

To run the use case, run (in Examples/Python/).

$ cd /path/to/shapeworks/Examples/Python
$ python lumps

This calls (in Examples/Python/) to perform the following.

  • Loads the lumps dataset using a local version if it exists (i.e., previously downloaded); otherwise, the dataset is automatically downloaded from the ShapeWorks Data Portal.
  • Optimizes particle distribution (i.e., the shape/correspondence model) by calling optimization functions in (in Examples/Python/). See Optimizing Shape Model for details about algorithmic parameters for optimizing the shape model.
  • Launches ShapeWorks Studio to visualize the use case results (i.e., the optimized shape model and the groomed data) by calling functions in (in Examples/Python/).

Grooming Data

This is a synthetic dataset that does not require grooming.

Optimizing Shape Model

Below are the default optimization parameters when running this use case. For a description of the optimize tool and its algorithmic parameters, see: How to Optimize Your Shape Model.

$ python lumps
        "number_of_particles": 512,
        "use_normals": 0,
        "normal_weight": 10.0,
        "checkpointing_interval": 100,
        "keep_checkpoints": 0,
        "iterations_per_split": 2000,
        "optimization_iterations": 500,
        "starting_regularization": 10,
        "ending_regularization": 1,
        "recompute_regularization_interval": 1,
        "domains_per_shape": 1,
        "domain_type": "mesh",
        "relative_weighting": 10,
        "initial_relative_weighting": 1,
        "procrustes_interval": 0,
        "procrustes_scaling": 0,
        "save_init_splits": 0,
        "verbosity": 1

Analyzing Shape Model

ShapeWorks Studio visualizes/analyzes the optimized particle-based shape model by visualizing the mean shape, individual shape samples, and the shape modes of variations. For more information, see: How to Analyze Your Shape Model?.

Here is the mean shape of the optimized shape mode using single-scale optimization. Note the two tiny lumps at the top, and towards the right.

Lumps Mean Shape

Here are lumps samples with their optimized correspondences. Lumps Samples

Here is a video showing the shape modes of variation (computed using principal component analysis - PCA) of the lumps dataset using single-scale optimization.