Dense 3D Face Correspondence

We present an algorithm that automatically establishes dense correspondences between a large number of 3D faces. Starting from automatically detected sparse correspondences on the outer boundary of 3D faces, the algorithm triangulates existing correspondences and expands them iteratively by matching points of distinctive surface curvature along the triangle edges. After exhausting keypoint matches, further correspondences are established by generating evenly distributed points within triangles by evolving level set geodesic curves from the centroids of large triangles. A deformable model (K3DM) is constructed from the dense corresponded faces and an algorithm is proposed for morphing the K3DM to fit unseen faces. This algorithm iterates between rigid alignment of an unseen face followed by regularized morphing of the deformable model. We have extensively evaluated the proposed algorithms on synthetic data and real 3D faces from the FRGCv2, Bosphorus, BU3DFE and UND Ear databases using quantitative and qualitative benchmarks. Our algorithm achieved dense correspondences with a mean localisation error of 1.28mm on synthetic faces and detected $14$ anthropometric landmarks on unseen real faces from the FRGCv2 database with 3mm precision. Furthermore, our deformable model fitting algorithm achieved 98.5% face recognition accuracy on the FRGCv2 and 98.6% on Bosphorus database. Our dense model is also able to generalize to unseen datasets.Comment: 24 Pages, 12 Figures, 6 Tables and 3 Algorithm

Keypoint-based deformation monitoring using a terrestrial laser scanner from a single station: Case study of a bridge pier

[EN] Terrestrial laser scanners (TLSs) offer a possibility for more automated and efficient deformation monitoring of civil engineering structures with higher spatial resolution than standard methods, as well as without the necessity of permanently installing the monitoring equipment. In such applications, scanners are usually placed so that the lines of sight are roughly aligned with the main directions of the expected deformations, and the deformations are estimated from point cloud differences between multiple epochs. This allows high sensitivity in the direction of the surface normal, but deformations along the surface are often undetected or hard to precisely quantify. In this work, we propose an algorithm based on the detection and matching of keypoints identified within TLS intensity images. This enables precise quantification of deformations along the scanned surfaces. We also present the application of the algorithm for monitoring a bridge pier of the Hochmoselbrücke in Germany, as a case study. Deformations up to about 4 cm due to thermal expansion and bending of the pier were successfully detected from scans taken throughout the day from a single location, up to 180 m from the monitored surfaces. The results agreed within a few millimeters to independent monitoring using state-of-the-art processing of TLS point clouds obtained from a different location and using a different type/brand of instrument. The newly proposed algorithm can either be used to complement existing TLS-based deformation analysis methods by adding sensitivity in certain directions, or it can be valuable as a standalone solution.Medic, T.; Ruttner, P.; Holst, C.; Wieser, A. (2023). Keypoint-based deformation monitoring using a terrestrial laser scanner from a single station: Case study of a bridge pier. En 5th Joint International Symposium on Deformation Monitoring (JISDM 2022). Editorial Universitat Politècnica de València. 167-175. https://doi.org/10.4995/JISDM2022.2022.1381216717

From 3D Point Clouds to Pose-Normalised Depth Maps

We consider the problem of generating either pairwise-aligned or pose-normalised depth maps from noisy 3D point clouds in a relatively unrestricted poses. Our system is deployed in a 3D face alignment application and consists of the following four stages: (i) data filtering, (ii) nose tip identification and sub-vertex localisation, (iii) computation of the (relative) face orientation, (iv) generation of either a pose aligned or a pose normalised depth map. We generate an implicit radial basis function (RBF) model of the facial surface and this is employed within all four stages of the process. For example, in stage (ii), construction of novel invariant features is based on sampling this RBF over a set of concentric spheres to give a spherically-sampled RBF (SSR) shape histogram. In stage (iii), a second novel descriptor, called an isoradius contour curvature signal, is defined, which allows rotational alignment to be determined using a simple process of 1D correlation. We test our system on both the University of York (UoY) 3D face dataset and the Face Recognition Grand Challenge (FRGC) 3D data. For the more challenging UoY data, our SSR descriptors significantly outperform three variants of spin images, successfully identifying nose vertices at a rate of 99.6%. Nose localisation performance on the higher quality FRGC data, which has only small pose variations, is 99.9%. Our best system successfully normalises the pose of 3D faces at rates of 99.1% (UoY data) and 99.6% (FRGC data)

Markerless deformation capture of hoverfly wings using multiple calibrated cameras

This thesis introduces an algorithm for the automated deformation capture of hoverfly wings from multiple camera image sequences. The algorithm is capable of extracting dense surface measurements, without the aid of fiducial markers, over an arbitrary number of wingbeats of hovering flight and requires limited manual initialisation. A novel motion prediction method, called the ‘normalised stroke model’, makes use of the similarity of adjacent wing strokes to predict wing keypoint locations, which are then iteratively refined in a stereo image registration procedure. Outlier removal, wing fitting and further refinement using independently reconstructed boundary points complete the algorithm. It was tested on two hovering data sets, as well as a challenging flight manoeuvre. By comparing the 3-d positions of keypoints extracted from these surfaces with those resulting from manual identification, the accuracy of the algorithm is shown to approach that of a fully manual approach. In particular, half of the algorithm-extracted keypoints were within 0.17mm of manually identified keypoints, approximately equal to the error of the manual identification process. This algorithm is unique among purely image based flapping flight studies in the level of automation it achieves, and its generality would make it applicable to wing tracking of other insects

Local feature based automatic target recognition for future 3D active homing seeker missiles

We propose an architecture appropriate for future Light Detection and Ranging (LIDAR) active homing seeker missiles with Automatic Target Recognition (ATR) capabilities. Our proposal enhances military targeting performance by extending ATR into the 3rd dimension. From a military and aerospace industry point of view, this is appealing as weapon effectiveness against camouflage, concealment and deception techniques can be substantially improved. Specifically, we present a missile seeker 3D ATR architecture that relies on the 3D local feature based SHOT descriptor and a dual-role pipeline with a number of pre and post-processing operations. We evaluate our architecture on a number of missile engagement scenarios in various environmental setups with the missile being under various altitudes, obliquities, distances to the target and scene resolutions. Under these demanding conditions, the recognition performance gained is highly promising. Even in the extreme case of reducing the database entries to a single template per target, our interchangeable ATR architecture still provides a highly acceptable performance. Although we focus on future intelligent missile systems, our approach can be implemented to a great range of time-critical complex systems for space, air and ground environments for military, law-enforcement, commercial and research purposes