jrmpc 0.1.0

PyTorch implementation of the JRMPC algorithm.

JRMPC PyTorch

Installation

pip install jrmpc

Presentation

This repos is a PyTorch portage of the JRMPC algorithm.

The two reference papers re:

• Georgios D. Evangelidis, D. Kounades-Bastian, R. Horaud, and E.Z Psarakis, A Generative Model for the Joint Registration of Multiple Point Sets, ECCV, 2014.
• Georgios D. Evangelidis, R. Horaud, Joint Alignment of Point Sets with Batch and Incremental Expectation-Maximization, PAMI, 2018.

The code provided by the authors is downloadable through this link.

Motivation

Leveraging PyTorch allows this implementation to support CUDA GPU. From my quick testing, it is approximately 50x faster than the base Matlab implementation.

Getting started

JRMPC is an algorith to jointly estimate rigid transformation aligning a set of point clouds of varying lengths. In the simplest setup, the following code is enough:

from jrmpc import jrmpc
V: list[Tensor] = load_views(...)  # list of M tensor (3, Nj).
R_hat, t_hat = jrmpc(V)  # R_hat is a tensor rotation (M, 3, 3) and t_hat is a tensor of translation (M, 3, 1).
V_registered = [r @ v + t for v, r, t in zip(views, R_hat, t_hat)]

I provide a small demo notebook with some visualizations.

Documentation

Here is the complete API description:

• V (Sequence[Tensor]): Views, sequence of M point clouds of varying length (3, Nj), j=0:M.
• X (Optional[Tensor]): Cluster centers. If None, computed internally.
• R (Optional[Tensor]): Initial rotations (M, 3, 3). If None, initialized with the identity matrix.
• t (Optional[Tensor]): Initial translations (M, 3). If None, t[j] is initialized with the arithmetic mean of V[j], i.e. as a centering operation (typically with V[j] of shape (3, N), t[j] is V[j].mean(dim=1)).
• S (Optional[Tensor]): Initial variances for the K GMM components. Either a tensor (K,) or a single scalar. If scalar is provided then all K components are initialized with the same variance. If None, all variances are initialized with the same value, which is computed as the squared length of the diagonal of the bounding box that contains all points of V, after applying initial rototranslation.
• max_num_iter (Optional[int]): Specifies the number of iterations, Default value: 100.
• epsilon (Optional[Tensor]): Artificial covariance flatten. A positive number added to S, after its update, at every iteration. Default value: 1e-6.
• initial_priors (Optional[Tensor]): Specifies the prior probabilities p of the GMM components, and implicitly defines the prior p_{K+1} for the outlier class. It can be a (K,) tensor or a scalar. If p is scalar then that same value is used for all components. The sum of all elements in p (or K*p if p is scalar), must be less than 1 as they represent a probability mass. p_{K+1} is computed internally as 1 - sum(p) if p is a vector, or as p_{K+1} = 1-K*p otherwise. gamma is uniquely defined from p_{K+1} as 1 = (gamma+1)*sum(p). Default value: The distribution of p_k is initialized as a uniform as p_k = 1/(K+1), k=0:K.
• gamma (Optional[float]): Positive scalar specifying the outlier proportion in V. Used to compute the prior probability p_{K+1} of the outlier component as gammasum_k(p_k). If gamma is provided then pk's are initialized uniformly as sum_k(p_k) = 1/(gamma+1) => p_k = 1/(K(gamma+1)). Paramater gamma is a shortcut to set initialPriors uniformly, and therefore, either 'gamma' or 'initialPriors' should be given at a time. Default value: 1/K.
• update_priors (bool, optional): It is a flag that controls the update of p across iterations. The algorithm expects a scalar. If it is (numeric) 0 then p is kept fixed otherwise priors are updated at every iteration. Default value: False.
• track_history (bool, optional): If True, keep track of the estimated transformation after each optimization step, and return the history. Default value: False.
• progress_bar (bool, optional): If True, display a progress bar during the max_num_iter optimization steps. Default value: False.