We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

/pdf/going-deeper-with-convolutions-1yobw2o2ds.pdf

Going deeper with convolutions

Incremental Structure-from-Motion is a prevalent strategy for 3D reconstruction from unordered image collections. While incremental reconstruction systems have tremendously advanced in all regards, robustness, accuracy, completeness, and scalability remain the key problems towards building a truly general-purpose pipeline. We propose a new SfM technique that improves upon the state of the art to make a further step towards this ultimate goal. The full reconstruction pipeline is released to the public as an open-source implementation.

/pdf/structure-from-motion-revisited-10382wusrz.pdf

Structure-from-Motion Revisited

Efficient view registration with respect to a given 3D reconstruction has many applications like inside-out tracking in indoor and outdoor environments, and geo-locating images from large photo collections. We present a fast location recognition technique based on structure from motion point clouds. Vocabulary tree-based indexing of features directly returns relevant fragments of 3D models instead of documents from the images database. Additionally, we propose a compressed 3D scene representation which improves recognition rates while simultaneously reducing the computation time and the memory consumption. The design of our method is based on algorithms that efficiently utilize modern graphics processing units to deliver real-time performance for view registration. We demonstrate the approach by matching hand-held outdoor videos to known 3D urban models, and by registering images from online photo collections to the corresponding landmarks.

/pdf/from-structure-from-motion-point-clouds-to-fast-location-366f2cowkb.pdf

From structure-from-motion point clouds to fast location recognition

Accurately determining the position and orientation from which an image was taken, i.e., computing the camera pose, is a fundamental step in many Computer Vision applications. The pose can be recovered from 2D-3D matches between 2D image positions and points in a 3D model of the scene. Recent advances in Structure-from-Motion allow us to reconstruct large scenes and thus create the need for image-based localization methods that efficiently handle large-scale 3D models while still being effective, i.e., while localizing as many images as possible. This paper presents an approach for large scale image-based localization that is both efficient and effective. At the core of our approach is a novel prioritized matching step that enables us to first consider features more likely to yield 2D-to-3D matches and to terminate the correspondence search as soon as enough matches have been found. Matches initially lost due to quantization are efficiently recovered by integrating 3D-to-2D search. We show how visibility information from the reconstruction process can be used to improve the efficiency of our approach. We evaluate the performance of our method through extensive experiments and demonstrate that it offers the best combination of efficiency and effectiveness among current state-of-the-art approaches for localization.

Efficient & Effective Prioritized Matching for Large-Scale Image-Based Localization

We address the problem of determining where a photo was taken by estimating a full 6-DOF-plus-intrincs camera pose with respect to a large geo-registered 3D point cloud, bringing together research on image localization, landmark recognition, and 3D pose estimation. Our method scales to datasets with hundreds of thousands of images and tens of millions of 3D points through the use of two new techniques: a co-occurrence prior for RANSAC and bidirectional matching of image features with 3D points. We evaluate our method on several large data sets, and show state-of-the-art results on landmark recognition as well as the ability to locate cameras to within meters, requiring only seconds per query.

/pdf/worldwide-pose-estimation-using-3d-point-clouds-38vtvjcjou.pdf

Worldwide pose estimation using 3d point clouds

Finding solutions to minimal problems for estimating epipolar geometry and camera motion leads to solving systems of algebraic equations. Often, these systems are not trivial and therefore special algorithms have to be designed to achieve numerical robustness and computational efficiency. The state of the art approach for constructing such algorithms is the Grobner basis method for solving systems of polynomial equations. Previously, the Grobner basis solvers were designed ad hoc for concrete problems and they could not be easily applied to new problems. In this paper we propose an automatic procedure for generating Grobner basis solvers which could be used even by non-experts to solve technical problems. The input to our solver generator is a system of polynomial equations with a finite number of solutions. The output of our solver generator is the Matlab or C code which computes solutions to this system for concrete coefficients. Generating solvers automatically opens possibilities to solve more complicated problems which could not be handled manually or solving existing problems in a better and more efficient way. We demonstrate that our automatic generator constructs efficient and numerically stable solvers which are comparable or outperform known manually constructed solvers. The automatic generator is available at http://cmp.felk.cvut.cz/minimal

/pdf/automatic-generator-of-minimal-problem-solvers-21kx8p1ha8.pdf

Automatic Generator of Minimal Problem Solvers

This paper presents a general solution to the determination of the pose of a perspective camera with unknown focal length from images of four 3D reference points Our problem is a generalization of the P3P and P4P problems previously developed for fully calibrated cameras Given four 2D-to-3D correspondences, we estimate camera position, orientation and recover the camera focal length We formulate the problem and provide a minimal solution from four points by solving a system of algebraic equations We compare the Hidden variable resultant and Grobner basis techniques for solving the algebraic equations of our problem By evaluating them on synthetic and on real-data, we show that the Grobner basis technique provides stable results

/pdf/a-general-solution-to-the-p4p-problem-for-camera-with-3hrsmbh8bl.pdf

A general solution to the P4P problem for camera with unknown focal length

In this paper we provide new fast and simple solutions to two important minimal problems in computer vision, the five-point relative pose problem and the six-point focal length problem. We show that these two problems can easily be formulated as polynomial eigenvalue problems of degree three and two and solved using standard efficient numerical algorithms. Our solutions are somewhat more stable than state-of-the-art solutions by Nister and Stewenius and are in some sense more straightforward and easier to implement since polynomial eigenvalue problems are well studied with many efficient and robust algorithms available. The quality of the solvers is demonstrated in experiments 1.

http://cmp.felk.cvut.cz/~kukelova/webthesis/publications/Kukelova-etal-BMVC-2008.pdf

Polynomial Eigenvalue Solutions to the 5-pt and 6-pt Relative Pose Problems.

The problem of determining the absolute position and orientation of a camera from a set of 2D-to-3D point correspondences is one of the most important problems in computer vision with a broad range of applications. In this paper we present a new solution to the absolute pose problem for camera with unknown radial distortion and unknown focal length from five 2D-to-3D point correspondences. Our new solver is numerically more stable, more accurate, and significantly faster than the existing state-of-the-art minimal four point absolute pose solvers for this problem. Moreover, our solver results in less solutions and can handle larger radial distortions. The new solver is straightforward and uses only simple concepts from linear algebra. Therefore it is simpler than the state-of-the-art Groebner basis solvers. We compare our new solver with the existing state-of-the-art solvers and show its usefulness on synthetic and real datasets.

/pdf/real-time-solution-to-the-absolute-pose-problem-with-unknown-10p5vmuj8d.pdf

Real-Time Solution to the Absolute Pose Problem with Unknown Radial Distortion and Focal Length

In this paper we provide new simple closed-form solutions to two minimal absolute pose problems for the case of known vertical direction In the first problem we estimate absolute pose of a calibrated camera from two 2D-3D correspondences and a given vertical direction In the second problem we assume camera with unknown focal length and radial distortion and estimate its pose together with the focal length and the radial distortion from three 2D-3D correspondences and a given vertical direction The vertical direction can be obtained either by direct physical measurement by, eg, gyroscopes and inertial measurement units or from vanishing points constructed in images Both our problems result in solving one polynomial equation of degree two in one variable and one, respectively two, systems of linear equations and can be efficiently solved in a closed-form By evaluating our algorithms on synthetic and real data we demonstrate that both our solutions are fast, efficient and numerically stabled

/pdf/closed-form-solutions-to-minimal-absolute-pose-problems-with-7tbfrp4erq.pdf

Zuzana Kukelova

Papers

Automatic Generator of Minimal Problem Solvers

A general solution to the P4P problem for camera with unknown focal length

Polynomial Eigenvalue Solutions to the 5-pt and 6-pt Relative Pose Problems.

Real-Time Solution to the Absolute Pose Problem with Unknown Radial Distortion and Focal Length

Closed-form solutions to minimal absolute pose problems with known vertical direction