In 1997, Azuma published a survey on augmented reality (AR). Our goal is to complement, rather than replace, the original survey by presenting representative examples of the new advances. We refer one to the original survey for descriptions of potential applications (such as medical visualization, maintenance and repair of complex equipment, annotation, and path planning); summaries of AR system characteristics (such as the advantages and disadvantages of optical and video approaches to blending virtual and real, problems in display focus and contrast, and system portability); and an introduction to the crucial problem of registration, including sources of registration error and error-reduction strategies.

https://www.cc.gatech.edu/~blair/papers/ARsurveyCGA.pdf

Recent advances in augmented reality

https://www.cs.ucla.edu/~dt/papers/mia96/mia96.pdf

Deformable models in medical image analysis: a survey

A morphable model for the synthesis of 3D faces

We propose a new model for active contours based on a geometric partial differential equation. Our model is intrinsec, stable (satisfies the maximum principle) and permits a rigorous mathematical analysis. It enables us to extract smooth shapes (we cannot retrieve angles) and it can be adapted to find several contours simultaneously. Moreover, as a consequence of the stability, we can design robust algorithms which can be engineed with no parameters in applications. Numerical experiments are presented.

A geometric model for active contours in image processing

Automatic generation and detection of highly reliable fiducial markers under occlusion

In this paper, we propose a method for coarse camera pose computation which is robust to viewing conditions and does not require a detailed model of the scene. This method meets the growing need of easy deployment of robotics or augmented reality applications in any environments, especially those for which no accurate 3D model nor huge amount of ground truth data are available. It exploits the ability of deep learning techniques to reliably detect objects regardless of viewing conditions. Previous works have also shown that abstracting the geometry of a scene of objects by an ellipsoid cloud allows to compute the camera pose accurately enough for various application needs. Though promising, these approaches use the ellipses fitted to the detection bounding boxes as an approximation of the imaged objects. In this paper, we go one step further and propose a learning-based method which detects improved elliptic approximations of objects which are coherent with the 3D ellipsoid in terms of perspective projection. Experiments prove that the accuracy of the computed pose significantly increases thanks to our method and is more robust to the variability of the boundaries of the detection boxes. This is achieved with very little effort in terms of training data acquisition -- a few hundred calibrated images of which only three need manual object annotation. Code and models are released at this https URL.

3D-Aware Ellipse Prediction for Object-Based Camera Pose Estimation

Active models for regularizing formant trajectories.

This paper presents an initial bimodal acoustic-visual synthesis system able to generate concurrently the speech signal and a 3D animation of the speaker's face. This is done by concatenating bimodal diphone units that consist of both acoustic and visual information. The latter is acquired using a stereovision technique. The proposed method addresses the problems of asyn- chrony and incoherence inherent in classic approaches to audiovisual synthesis. Unit selection is based on classic target and join costs from acoustic-only synthesis, which are augmented with a visual join cost. Preliminary results indicate the benefits of this approach, since both the synthesized speech signal and the face animation are of good quality.

https://hal.inria.fr/inria-00526782/document

Towards a True Acoustic-Visual Speech Synthesis

We consider the problem of camera pose estimation from a scene model obtained beforehand by a structure-from-motion (SfM) algorithm. The model is made of 3D points, each one of them being represented by its coordinates and a set of photometric descriptors such as SIFT, extracted from some of the input images of the SfM stage. Pose estimation is based on the matching of interest points from a test view with model points, using the descriptors. Descriptors having a limited invariance with respect to viewpoint changes, such an approach is likely to fail when the test view is far away from the images used to construct the model. Viewpoint simulation techniques, as ASIFT, have proved effective for wide-baseline image matching. This paper explores how these techniques can enrich a scene model by adding descriptors from simulated views, using either orthographic or pinhole virtual cameras. Viewpoint simulation permits pose estimation in situations where the approach based on the sole SIFT descriptors simply fails.

/pdf/viewpoint-simulation-for-camera-pose-estimation-from-an-45lk84rfai.pdf

Viewpoint simulation for camera pose estimation from an unstructured scene model

The purpose of this study is to determine the intra- and inter-observer variability in the manual delineation of the boundaries of brain arterio-venous malformations (AVMs) on digital subtracted angiograms. Such delineation is used to define the target volume in stereotactic radiotherapy.

https://hal.inria.fr/inria-00107673/document

Marie-Odile Berger

Papers

3D-Aware Ellipse Prediction for Object-Based Camera Pose Estimation

Active models for regularizing formant trajectories.

Towards a True Acoustic-Visual Speech Synthesis

Viewpoint simulation for camera pose estimation from an unstructured scene model

Intra- and intre-observer variability in the angiographic delineation of brain arterio-venous malformations.