DeepAI AI Chat
Log In Sign Up

Robust Rigid Point Registration based on Convolution of Adaptive Gaussian Mixture Models

by   Can Pu, et al.

Matching 3D rigid point clouds in complex environments robustly and accurately is still a core technique used in many applications. This paper proposes a new architecture combining error estimation from sample covariances and dual global probability alignment based on the convolution of adaptive Gaussian Mixture Models (GMM) from point clouds. Firstly, a novel adaptive GMM is defined using probability distributions from the corresponding points. Then rigid point cloud alignment is performed by maximizing the global probability from the convolution of dual adaptive GMMs in the whole 2D or 3D space, which can be efficiently optimized and has a large zone of accurate convergence. Thousands of trials have been conducted on 200 models from public 2D and 3D datasets to demonstrate superior robustness and accuracy in complex environments with unpredictable noise, outliers, occlusion, initial rotation, shape and missing points.


DUGMA: Dynamic Uncertainty-Based Gaussian Mixture Alignment

Registering accurately point clouds from a cheap low-resolution sensor i...

Overlap-guided Gaussian Mixture Models for Point Cloud Registration

Probabilistic 3D point cloud registration methods have shown competitive...

Direct Fitting of Gaussian Mixture Models

When fitting Gaussian Mixture Models to 3D geometry, the model is typica...

DeepGMR: Learning Latent Gaussian Mixture Models for Registration

Point cloud registration is a fundamental problem in 3D computer vision,...

An Adaptive Data Representation for Robust Point-Set Registration and Merging

This paper presents a framework for rigid point-set registration and mer...

Probabilistic Point Cloud Modeling via Self-Organizing Gaussian Mixture Models

This letter presents a continuous probabilistic modeling methodology for...

Deep Weighted Consensus: Dense correspondence confidence maps for 3D shape registration

We present a new paradigm for rigid alignment between point clouds based...