Towards large-scale geometry indexing by feature selection

We present a new approach to image indexing and retrieval, which integrates appearance with global image geometry in the indexing process, while enjoying robustness against viewpoint change, photometric variations, occlusion, and background clutter. We exploit shape parameters of local features to estimate image alignment via a single correspondence. Then, for each feature, we construct a sparse spatial map of all remaining features, encoding their normalized position and appearance, typically vector quantized to visual word. An image is represented by a collection of such feature maps and RANSAC-like matching is reduced to a number of set intersections. The required index space is still quadratic in the number of features. To make it linear, we propose a novel feature selection model tailored to our feature map representation, replacing our earlier hashing approach. The resulting index space is comparable to baseline bag-of-words, scaling up to one million images while outperforming the state of the art on three publicly available datasets. To our knowledge, this is the first geometry indexing method to dispense with spatial verification at this scale, bringing query times down to milliseconds

A Comparative Study of Registration Methods for RGB-D Video of Static Scenes

The use of RGB-D sensors for mapping and recognition tasks in robotics or, in general, for virtual reconstruction has increased in recent years. The key aspect of these kinds of sensors is that they provide both depth and color information using the same device. In this paper, we present a comparative analysis of the most important methods used in the literature for the registration of subsequent RGB-D video frames in static scenarios. The analysis begins by explaining the characteristics of the registration problem, dividing it into two representative applications: scene modeling and object reconstruction. Then, a detailed experimentation is carried out to determine the behavior of the different methods depending on the application. For both applications, we used standard datasets and a new one built for object reconstruction.This work has been supported by a grant from the Spanish Government, DPI2013-40534-R, University of Alicante projects GRE11-01 and a grant from the Valencian Government, GV/2013/005

Single-Image Depth Prediction Makes Feature Matching Easier

Good local features improve the robustness of many 3D re-localization and multi-view reconstruction pipelines. The problem is that viewing angle and distance severely impact the recognizability of a local feature. Attempts to improve appearance invariance by choosing better local feature points or by leveraging outside information, have come with pre-requisites that made some of them impractical. In this paper, we propose a surprisingly effective enhancement to local feature extraction, which improves matching. We show that CNN-based depths inferred from single RGB images are quite helpful, despite their flaws. They allow us to pre-warp images and rectify perspective distortions, to significantly enhance SIFT and BRISK features, enabling more good matches, even when cameras are looking at the same scene but in opposite directions.Comment: 14 pages, 7 figures, accepted for publication at the European conference on computer vision (ECCV) 202

View synthesis for pose computation

International audienceGeometrical registration of a query image with respect to a 3D model, or pose estimation, is the cornerstone of many computer vision applications. It is often based on the matching of local photometric descriptors invariant to limited viewpoint changes. However, when the query image has been acquired from a camera position not covered by the model images, pose estimation is often not accurate and sometimes even fails, precisely because of the limited invariance of descriptors. In this paper, we propose to add descriptors to the model, obtained from synthesized views associated with virtual cameras completing the covering of the scene by the real cameras. We propose an efficient strategy to localize the virtual cameras in the scene and generate valuable descriptors from synthetic views. We also discuss a guided sampling strategy for registration in this context. Experiments show that the accuracy of pose estimation is dramatically improved when large viewpoint changes makes the matching of classic descriptors a challenging task

A Study of Projections for Key Point Based Registration of Panoramic Terrestrial 3D Laser Scans

Abstract This paper surveys state of the art image features and descriptors for the task of 3D scan registration based on panoramic reflectance images. As modern terrestrial laser scanners digitize their environment in a spherical way, the sphere has to be projected to a two-dimensional image. To this end, we evaluate the equirectangular, the cylindrical, the Mercator, the rectilinear, the Pannini, the stereographic, and the z-axis projection. We show that the Mercator and the Pannini projection outperform the other projection methods

Punktkorrespondenzen in Bildpaaren aus projektiven und radiometrischen Invarianzen

Eine fundamentale Voraussetzung für sehr viele Anwendungen in der Photogrammetrie und in der Computer Vision ist es, identische Punkte eines abgebildeten Objektes in zwei sich überlappenden Bildern zu finden. Die Ergebnisse der Arbeit und die durchgeführten Experimente zeigen, dass mittels kombinierter projektiv invarianter Merkmale Punktzuordnungen gefunden werden können, welche mit bisherigen Verfahren nicht möglich waren

Geometry-driven feature detection

Matching images taken from different viewpoints is a fundamental step for many computer vision applications including 3D reconstruction, scene recognition, virtual reality, robot localization, etc. The typical approaches detect feature keypoints based on local properties to achieve robustness to viewpoint changes, and establish correspondences between keypoints to recover the 3D geometry or determine the similarity between images. The complexity of perspective distortion challenges the detection of viewpoint invariant features; the lack of 3D geometric information about local features makes their matching inefficient. In this thesis, I explore feature detection based on 3D geometric information for improved projective invariance. The main novel research contributions of this thesis are as follows. First, I give a projective invariant feature detection method that exploits 3D structures recovered from simple stereo matching. By leveraging the rich geometric information of the detected features, I present an efficient 3D matching algorithm to handle large viewpoint changes. Second, I propose a compact high-level feature detector that robustly extracts repetitive structures in urban scenes, which allows efficient wide-baseline matching. I further introduce a novel single-view reconstruction approach to recover the 3D dense geometry of the repetition-based features

Feature regression for continuous pose estimation of object categories

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Robust 3D Object Pose Estimation and Tracking from Monocular Images in Industrial Environments

Recent advances in Computer Vision are changing our way of living and enabling new applications for both leisure and professional use. Regrettably, in many industrial domains the spread of state-of-the-art technologies is made challenging by the abundance of nuisances that corrupt existing techniques beyond the required dependability. This is especially true for object localization and tracking, that is, the problem of detecting the presence of objects on images and videos and estimating their pose. This is a critical task for applications such as Augmented Reality (AR), robotic autonomous navigation, robotic object grasping, or production quality control; unfortunately, the reliability of existing techniques is harmed by visual features such as the abundance of specular and poorly textured objects, cluttered scenes, or artificial and in-homogeneous lighting. In this thesis, we propose two methods for robustly estimating the pose of a rigid object under the challenging conditions typical of industrial environments. Both methods rely on monocular images to handle metallic environments, on which depth cameras would fail; both are conceived with a limited computational and memory footprint, so that they are suitable for real-time applications such as AR. We test our methods on datasets issued from real user case scenarios, exhibiting challenging conditions. The first method is based on a global image alignment framework and a robust dense descriptor. Its global approach makes it robust in presence of local artifacts such as specularities appearing on metallic objects, ambiguous patterns like screws or wires, and poorly textured objects. Employing a global approach avoids the need of reliably detecting and matching local features across images, that become ill-conditioned tasks in the considered environments; on the other hand, current methods based on dense image alignment usually rely on luminous intensities for comparing the pixels, which is not robust in presence of challenging illumination artifacts. We show how the use of a dense descriptor computed as a non-linear function of luminous intensities, that we refer to as ``Descriptor Fields'', greatly enhances performances at a minimal computational overhead. Their low computational complexity and their ease of implementation make Descriptor Fields suitable for replacing intensities in a wide number of state-of-the-art techniques based on dense image alignment. Relying on a global approach is appropriate for overcoming local artifacts, but it can be un-effective when the target object undergoes extreme occlusions in cluttered environments. For this reason, we propose a second approach based on the detection of discriminative object parts. At the core of our approach is a novel representation for the 3D pose of the parts, that allows us to predict the 3D pose of the object even when only a single part is visible; when several parts are visible, we can easily combine them to compute a better pose of the object. The 3D pose we obtain is usually very accurate, even when only few parts are visible. We show how to use this representation in a robust 3D tracking framework. In addition to extensive comparisons with the state-of-the-art, we demonstrate our method on a practical Augmented Reality application for maintenance assistance in the ATLAS particle detector at CERN