Algorithms For Automatic And Robust Registration Of 3D Head Scans

wo methods for registering laser-scans of human heads and transforming them to a new semantically consistent topology defined by a user-provided template mesh are described. Both algorithms are stated within the Iterative Closest Point framework. The first method is based on finding landmark correspondences by iteratively registering the vicinity of a landmark with a re-weighted error function. Thin-plate spline interpolation is then used to deform the template mesh and finally the scan is resampled in the topology of the deformed template. The second algorithm employs a morphable shape model, which can be computed from a database of laser-scans using the first algorithm. It directly optimizes pose and shape of the morphable model. The use of the algorithm with PCA mixture models, where the shape is split up into regions each described by an individual subspace, is addressed. Mixture models require either blending or regularization strategies, both of which are described in detail. For both algorithms, strategies for filling in missing geometry for incomplete laser-scans are described. While an interpolation-based approach can be used to fill in small or smooth regions, the model-driven algorithm is capable of fitting a plausible complete head mesh to arbitrarily small geometry, which is known as "shape completion". The importance of regularization in the case of extreme shape completion is shown.

Evaluation of Binocular Eye Trackers and Algorithms for 3D Gaze Interaction in Virtual Reality Environments

Tracking user’s visual attention is a fundamental aspect in novel human-computer interaction paradigms found in Virtual Reality. For example, multimodal interfaces or dialogue-based communications with virtual and real agents greatly benefit from the analysis of the user’s visual attention as a vital source for deictic references or turn-taking signals. Current approaches to determine visual attention rely primarily on monocular eye trackers. Hence they are restricted to the interpretation of two-dimensional fixations relative to a defined area of projection. The study presented in this article compares precision, accuracy and application performance of two binocular eye tracking devices. Two algorithms are compared which derive depth information as required for visual attention-based 3D interfaces. This information is further applied to an improved VR selection task in which a binocular eye tracker and an adaptive neural network algorithm is used during the disambiguation of partly occluded objects.

A Video Database for the Development of Stereo-3D Post-Production Algorithms

This paper introduces a database of freely available stereo-3D content designed to facilitate research in stereo post-production. It describes the structure and content of the database and provides some details about how the material was gathered. The database includes examples of many of the scenarios characteristic to broadcast footage. Material was gathered at different locations including a studio with controlled lighting and both indoor and outdoor on-location sites with more restricted lighting control. The database also includes video sequences with accompanying 3D audio data recorded in an Ambisonics format. An intended consequence of gathering the material is that the database contains examples of degradations that would be commonly present in real-world scenarios. This paper describes one such artefact caused by uneven exposure in the stereo views, causing saturation in the over-exposed view. An algorithm for the restoration of this artefact is proposed in order to highlight the usefuiness of the database.

A user supported object tracking framework for interactive video production

We present a user supported tracking framework that combines automatic tracking with extended user input to create error free tracking results that are suitable for interactive video production. The goal of our approach is to keep the necessary user input as small as possible. In our framework, the user can select between different tracking algorithms - existing ones and new ones that are described in this paper. Furthermore, the user can automatically fuse the results of different tracking algorithms with our robust fusion approach. The tracked object can be marked in more than one frame, which can significantly improve the tracking result. After tracking, the user can validate the results in an easy way, thanks to the support of a powerful interpolation technique. The tracking results are iteratively improved until the complete track has been found. After the iterative editing process the tracking result of each object is stored in an interactive video file that can be loaded by our player for interactive videos.