In this paper an optimal method for distributed collision avoidance among multiple non-holonomic robots is presented in theory and experiments. Non-holonomic optimal reciprocal collision avoidance (NH-ORCA) builds on the concepts introduced in [2], but further guarantees smooth and collision-free motions under non-holonomic constraints. Optimal control inputs and constraints in velocity space are formally derived for the non-holonomic robots. The theoretical results are validated in several collision avoidance experiments with up to fourteen e-puck robots set on collision course. Even in scenarios with very crowded situations, NH-ORCA showed to be collision-free for all times.
We present a new technique for passive and markerless facial performance capture based on anchor frames . Our method starts with high resolution per-frame geometry acquisition using state-of-the-art stereo reconstruction, and proceeds to establish a single triangle mesh that is propagated through the entire performance. Leveraging the fact that facial performances often contain repetitive subsequences, we identify anchor frames as those which contain similar facial expressions to a manually chosen reference expression. Anchor frames are automatically computed over one or even multiple performances. We introduce a robust image-space tracking method that computes pixel matches directly from the reference frame to all anchor frames, and thereby to the remaining frames in the sequence via sequential matching. This allows us to propagate one reconstructed frame to an entire sequence in parallel, in contrast to previous sequential methods. Our anchored reconstruction approach also limits tracker drift and robustly handles occlusions and motion blur. The parallel tracking and mesh propagation offer low computation times. Our technique will even automatically match anchor frames across different sequences captured on different occasions, propagating a single mesh to all performances.
Abstract. A method for matching image primitives through a sequence is described, for the purpose of acquiring 3D geometric models. The method includes a novel robust estimator of the trifocal tensor, based on a minimum number of token correspondences across an image triplet; and a novel tracking algorithm in which corners and line segments are matched over image triplets in an integrated framework. The matching techniques are both robust (detecting and discarding mismatches) and fully automatic. The matched tokens are used to compute 3D structure, which is initialised as it appears and then recursively updated over time. The approach is uncalibrated -camera internal parameters and camera motion are not known or required. Experimental results are provided for a variety of scenes, including outdoor scenes taken with a hand-held camcorder. Quantitative statistics are included to assess the matching performance, and renderings of the 3D structure enable a qualitative assessment of the results. 1Introduction The aim of this work is to recover 3D models from long uncalibrated monocular image sequences. These models will be used for graphics and virtual reality applications. The sequences are generated by circumnavigating the object of interest (e.g. a house) acquiring images with a camcorder. Neither the internal calibration of the camera nor the motion of the camera are known. In particular the motion is unlikely to be smooth. The focus of this paper is on the matching and tracking of image primitives which underpins the structure recovery.We build on the work of a number of previous successful systems which have recovered structure and motion from tracked image primitives (tokens). Coarsely these systems can be divided into those that use sequential [1,3,4,10,19,28], and those that use batch updates [13,15,23]. Here the matching and structure update are sequential. However, the basic unit is an image triplet, rather than the more usual image pair. It is in this area of tracking technology [26,27] that we have made the most significant innovations. A finite displacement (several cms) between views prohibits the utilisation of simple nearest neighbour token matching strategy between consecutive images. In this work:1. Corner and line segments are matched simultaneously over image triplets in an integrated framework, by employing the trifocal tensor [14,21,22]. 2. A robust estimator for the trifocal tensor is developed, with the tensor instantiated over three views using a minimal set (six) of point matches and a RANSAC scheme.
This technical report describes a passive stereo system for capturing the 3D geometry of a face in a single-shot under standard light sources. The system is low-cost and easy to deploy. Results are sub-millimeter accurate and commensurate with those from stateof-the-art systems based on active lighting, and the models meet the quality requirements of a demanding domain like the movie industry. Recovered models are shown for captures from both high-end cameras in a studio setting and from a consumer binocular-stereo camera, demonstrating scalability across a spectrum of camera deployments, and showing the potential for 3D face modeling to move beyond the professional arena and into the emerging consumer market in stereoscopic photography.Our primary technical contribution is a modification of standard stereo refinement methods to capture pore-scale geometry, using a qualitative approach that produces visually realistic results. The second technical contribution is a calibration method suited to face capture systems. The systemic contribution includes multiple demonstrations of system robustness and quality. These include capture in a studio setup, capture off a consumer binocular-stereo camera, scanning of faces of varying gender and ethnicity and age, capture of highly-transient facial expression, and scanning a physical mask to provide ground-truth validation.
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