Hierarchical structure-and-motion recovery from uncalibrated images

This paper addresses the structure-and-motion problem, that requires to find camera motion and 3D struc- ture from point matches. A new pipeline, dubbed Samantha, is presented, that departs from the prevailing sequential paradigm and embraces instead a hierarchical approach. This method has several advantages, like a provably lower computational complexity, which is necessary to achieve true scalability, and better error containment, leading to more stability and less drift. Moreover, a practical autocalibration procedure allows to process images without ancillary information. Experiments with real data assess the accuracy and the computational efficiency of the method.Comment: Accepted for publication in CVI

Geometry and appearance modelling from images

Non disponibileThis thesis is about the acquisition of a textured 3D surface model from a sequence of sparse images, in the direction of a completely un- supervised approach. It offers original contributions in the following fields. Autocalibration. The problem of autocalibration of a moving cam- era with unknown constant intrinsic parameters is here addressed. Existing autocalibration techniques use numerical optimization al- gorithms whose convergence to the correct result cannot be guaran- teed, in general. To face this problem, a method where an interval branch-and-bound method is employed for numerical minimization has been deveoped. Thanks to the properties of Interval Analysis this method converges to the global solution with mathematical certainty and arbitrary accuracy, and the only input information it requires from the user are a set of point correspondences and a search interval. Triangulation. We deal with the problem of an accurate estimation of 3D points coordinates that rigorously takes into account the propa- gation of data errors and roundoff. Image points are represented as small rectangles: as a result, the output of the n-views triangulation is not a single point in space, but a polyhedron that contains all the possible solutions. Computational Geometry techniques are used to estimate this polyhedron. Constraint-based reconstruction. We address the problem of un- supervised constrained scene modelling from many calibrated views. Given connectivity information, planes are detected from an ap- proximate model and geometric constraints are imposed on such planes. Then, these constraints are used as well with the polyhe- dral bounds for 3D points to obtain a faithful 3D model. All this process is completely automatic, with a further step of constraints simplification, aiming at eliminating redundancies and erroneous contraints. Reflectance recovery. The problem of eliminating lighting artefacts from the input images of the 3D model, obtaining normalized tex- ture maps, which can be re-illuminated, is here addressed. Assum- ing Lambertian surfaces, we separate reflectance information from shading, forming two separate images. The approach is based on the binary classification of image derivatives, with the help of the grayscale image, invariant to illumination, suggested by Finlayson et al. The results obtained are comparable with the state of the art, but they are obtained without any learning processes to classify derivatives

Stabilizing 3D modeling with geometric constraints propagation

This paper proposes a technique for estimating piecewise planar models of objects from their images and geometric constraints. First, assuming a bounded noise in the localization of 2D points, the position of the 3D point is estimated as a poly- hedron containing all the possible solutions of the triangulation. Then, given the topological structure of the 3D points cloud, geometric relationships among facets, such as coplanarity, parallelism, orthogonality, and angle equality, are automatically detected. A subset of them that is sufficient to stabilize the 3D model estimation is selected with a flow-network based algorithm. Finally a feasible instance of the 3D model, i.e. one that satisfies the geometric constraints and whose 3D vertices lie within the associated polyhedral bounds, is computed by solving a Constraint Satisfaction Problem. The process accommodates uncertainty in a non-probabilistic fashion and thus provides rigorous results. Synthetic and real experiments illustrate the approach

Structure-and-Motion Pipeline on a Hierarchical Cluster Tree

This papers introduces a novel hierarchical scheme for computing Structure and Motion. The images are organized into a tree with agglomerative clustering, using a measure of overlap as the distance. The reconstruction then follows this tree from the leaves to the root. As a result, the problems is broken into smaller instances, which are then separately solved and combined. Compared to the standard sequential approach, this framework has a lower computational complexity, it is independent from the initial pair of views, and copes better with drift problems. A formal complexity analysis and some experimental results support these claims. 1

Automatically Smoothing Camera Pose Using Cross-Validation for Sequential Vision-Based 3D Mapping

International audienc

AUTOMATIC STRUCTURE RECOVERY AND VISUALIZATION

We describe an automated pipeline for the reconstruction and rendering of three dimensional objects, with particular emphasys for urban environments. Our system can robustly recover 3D points and cameras from uncalibrated views, without manual assistance. The reconstructed structure is augmented by fitting geometrical primitives such as planes and cylinders to the sparse point cloud obtained. Such information is the key to obtain a higher level understanding of the scene; we use this knowledge to efficiently render the recovered environment, capturing its global appearance while preserving scalability. Several examples display our system in action.

A re-evaluation of pedestrian detection on riemannian manifolds

Boosting covariance data on Riemannian manifolds has proven to be a convenient strategy in a pedestrian detection context. In this paper we show that the detection performances of the state-of-the-art approach of Tuzel et al. can be greatly improved, from both a computational and a qualitative point of view, by considering practical and theoretical issues, and allowing also the estimation of occlusions in a fine way. The resulting detection system reaches the best performance on the INRIA dataset, setting novel state-of-the art results

Part-based human detection on Riemannian Manifolds

In this paper we propose a novel part-based framework for pedestrian detection. We model a human as a hierarchy of \ufb01xed overlapped parts, each of which described by covari- ances of features. Each part is modeled by a boosted classi- \ufb01er, learnt using Logitboost on Riemannian manifolds. All the classi\ufb01ers are then linked to form a high-level classi\ufb01er, through weighted summation, whose weights are estimated during the learning. The \ufb01nal classi\ufb01er is simple, light and robust. The experimental results show that we outperform the state-of-the-art human detection performances on the INRIA person dataset