Active nonrigid ICP algorithm

© 2015 IEEE.The problem of fitting a 3D facial model to a 3D mesh has received a lot of attention the past 15-20 years. The majority of the techniques fit a general model consisting of a simple parameterisable surface or a mean 3D facial shape. The drawback of this approach is that is rather difficult to describe the non-rigid aspect of the face using just a single facial model. One way to capture the 3D facial deformations is by means of a statistical 3D model of the face or its parts. This is particularly evident when we want to capture the deformations of the mouth region. Even though statistical models of face are generally applied for modelling facial intensity, there are few approaches that fit a statistical model of 3D faces. In this paper, in order to capture and describe the non-rigid nature of facial surfaces we build a part-based statistical model of the 3D facial surface and we combine it with non-rigid iterative closest point algorithms. We show that the proposed algorithm largely outperforms state-of-the-art algorithms for 3D face fitting and alignment especially when it comes to the description of the mouth region

Slippage Features

In this report, we present a novel feature detection technique for unstructured point clouds. We introduce a generalized concept of geometric features that detects locally uniquely identifiable keypoints as centroids of area with locally minimal slippage. We extend the concept to multiple scales and extract features using multi-scale mean shift clustering. In order to validate matches between feature points, we employ a two stage technique that first sorts out unlikely matches, followed by an approximate alignment between remaining features by a rotational cross-correlation analysis and a local iterative closest point (ICP) registration. The resulting residuals are then used as final similarity measure. The proposed combination of techniques results in a robust and reliable correspondence detection technique that yields registration results in situations where previous techniques are not able to detect usable feature correspondences. We provide a detailed empirical analysis of the method, and apply the technique to global registration, symmetry detection and deformable matching problems

GRASP News Volume 9, Number 1

A report of the General Robotics and Active Sensory Perception (GRASP) Laboratory