Gilles Simon

Matthieu Zins, Gilles Simon, Marie-Odile Berger

In this work, we explore the use of objects in Simultaneous Localization and Mapping in unseen worlds and propose an object-aided system (OA-SLAM). More precisely, we show that, compared to low-level points, the major benefit of objects lies in their higher-level semantic and discriminating power. Points, on the contrary, have a better spatial localization accuracy than the generic coarse models used to represent objects (cuboid or ellipsoid). We show that combining points and objects is of great interest to address the problem of camera pose recovery. Our main contributions are: (1) we improve the relocalization ability of a SLAM system using high-level object landmarks; (2) we build an automatic system, capable of identifying, tracking and reconstructing objects with 3D ellipsoids; (3) we show that object-based localization can be used to reinitialize or resume camera tracking. Our fully automatic system allows on-the-fly object mapping and enhanced pose tracking recovery, which we think, can significantly benefit to the AR community. Our experiments show that the camera can be relocalized from viewpoints where classical methods fail. We demonstrate that this localization allows a SLAM system to continue working despite a tracking loss, which can happen frequently with an uninitiated user. Our code and test data are released at gitlab.inria.fr/tangram/oa-slam.

Matthieu Zins, Gilles Simon, Marie-Odile Berger

In this paper, we propose a method for initial camera pose estimation from just a single image 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 ellipsoids in terms of perspective projection. Experiments prove that the accuracy of the computed pose significantly increases thanks to our method. 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 https://gitlab.inria.fr/tangram/3d-aware-ellipses-for-visual-localization

Vincent Gaudillière, Gilles Simon, Marie-Odile Berger

In computer vision, camera pose estimation from correspondences between 3D geometric entities and their projections into the image has been a widely investigated problem. Although most state-of-the-art methods exploit low-level primitives such as points or lines, the emergence of very effective CNN-based object detectors in the recent years has paved the way to the use of higher-level features carrying semantically meaningful information. Pioneering works in that direction have shown that modelling 3D objects by ellipsoids and 2D detections by ellipses offers a convenient manner to link 2D and 3D data. However, the mathematical formalism most often used in the related litterature does not enable to easily distinguish ellipsoids and ellipses from other quadrics and conics, leading to a loss of specificity potentially detrimental in some developments. Moreover, the linearization process of the projection equation creates an over-representation of the camera parameters, also possibly causing an efficiency loss. In this paper, we therefore introduce an ellipsoid-specific theoretical framework and demonstrate its beneficial properties in the context of pose estimation. More precisely, we first show that the proposed formalism enables to reduce the pose estimation problem to a position or orientation-only estimation problem in which the remaining unknowns can be derived in closed-form. Then, we demonstrate that it can be further reduced to a 1 Degree-of-Freedom (1DoF) problem and provide the analytical derivations of the pose as a function of that unique scalar unknown. We illustrate our theoretical considerations by visual examples and include a discussion on the practical aspects. Finally, we release this paper along with the corresponding source code in order to contribute towards more efficient resolutions of ellipsoid-related pose estimation problems.

Matthieu Zins, Gilles Simon, Marie-Odile Berger

In this paper, we propose an object-based camera pose estimation from a single RGB image and a pre-built map of objects, represented with ellipsoidal models. We show that contrary to point correspondences, the definition of a cost function characterizing the projection of a 3D object onto a 2D object detection is not straightforward. We develop an ellipse-ellipse cost based on level sets sampling, demonstrate its nice properties for handling partially visible objects and compare its performance with other common metrics. Finally, we show that the use of a predictive uncertainty on the detected ellipses allows a fair weighting of the contribution of the correspondences which improves the computed pose. The code is released at https://gitlab.inria.fr/tangram/level-set-based-camera-pose-estimation.

Matthieu Zins, Gilles Simon, Marie-Odile Berger

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 https://github.com/zinsmatt/3D-Aware-Ellipses-for-Visual-Localization.

Antoine Fond, Marie-Odile Berger, Gilles Simon

This paper presents an efficient approach for solving jointly facade registration and semantic segmentation. Progress in facade detection and recognition enable good initialization for the registration of a reference facade to a newly acquired target image. We propose here to rely on semantic segmentation to improve the accuracy of that initial registration. Simultaneously we aim to improve the quality of the semantic segmentation through the registration. These two problems are jointly solved in a Expectation-Maximization framework. We especially introduce a bayesian model that use prior semantic segmentation as well as geometric structure of the facade reference modeled by $L_p$ Gaussian Mixtures. We show the advantages of our method in term of robustness to clutter and change of illumination on urban images from various database.