We propose a new algorithm for finding an unknown number of geometric models, e.g., homographies. The problem is formalized as finding dominant model instances progressively without forming crisp point-to-model assignments. Dominant instances are found via a RANSAC-like sampling and a consolidation process driven by a model quality function considering previously proposed instances. New ones are found by clustering in the consensus space. This new formulation leads to a simple iterative algorithm with state-of-the-art accuracy while running in real-time on a number of vision problems - at least two orders of magnitude faster than the competitors on two-view motion estimation. Also, we propose a deterministic sampler reflecting the fact that real-world data tend to form spatially coherent structures. The sampler returns connected components in a progressively densified neighborhood-graph. We present a number of applications where the use of multiple geometric models improves accuracy. These include pose estimation from multiple generalized homographies; trajectory estimation of fast-moving objects; and we also propose a way of using multiple homographies in global SfM algorithms. Source code: https://github.com/danini/clustering-in-consensus-space.

We propose ways to speed up the initial pose-graph generation for global Structure-from-Motion algorithms. To avoid forming tentative point correspondences by FLANN and geometric verification by RANSAC, which are the most time-consuming steps of the pose-graph creation, we propose two new methods – built on the fact that image pairs usually are matched consecutively. Thus, candidate relative poses can be recovered from paths in the partly-built pose-graph. We propose a heuristic for the A*traversal, considering global similarity of images and the quality of the pose-graph edges. Given a relative pose from a path, descriptor-based feature matching is made "light-weight" by exploiting the known epipolar geometry. To speed up PROSAC-based sampling when RANSAC is applied, we propose a third method to order the correspondences by their inlier probabilities from previous estimations. The algorithms are tested on 402130 image pairs from the 1DSfM dataset and they speed up the feature matching 17 times and pose estimation 5 times. Source code: https://github.com/danini/pose-graph-initialization

An optimal, in the least squares sense, method is proposed to estimate surface normals in both stereo and multi-view cases. The proposed algorithm exploits exclusively photometric information via affine correspondences and estimates the normal for each correspondence independently. The normal is obtained as a root of a quartic polynomial. Therefore, the processing time is negligible. Eliminating the outliers, we propose a robust extension of the algorithm that combines maximum likelihood estimation and iteratively re-weighted least squares. The method has been validated on both synthetic and publicly available real-world datasets. It is superior to the state of the art in terms of accuracy and processing time. Besides, we demonstrate two possible applications: 1) using our algorithm as the seed-point generation step of patch-based multi-view stereo method, the obtained reconstruction is more accurate, and the error of the 3D points is reduced by 30% on average and 2) multi-plane fitting becomes more accurate applied to the resulting oriented point cloud.

A new camera calibration approach is proposed that can utilize the affine transformations and surface normals of small spatial patches. Even though classical calibration algorithms use only point locations, images contain more information than simple 2D point coordinates. New methods are presented in this paper for the calibration problem with their closed-form solutions, then the estimated parameters are numerically refined. The accuracy of our novel methods is validated on synthesized testing data, and the real-world applicability is presented on the calibration of a 3D structured-light scanner.

Sensor fusion is an important part of modern cyber-physical systems that observe and analyse real-world environments. Time-of-Flight depth cameras provide high frame rate low-resolution depth data that can be efficiently used in many applications related to cyber-physical systems. In this paper, we address the critical issue of upsampling and enhancing the low-quality depth data using a calibrated and registered high-resolution colour image or video. Two novel algorithms for image-guided depth upsampling are proposed based on different principles. A new method for video-guided upsampling is also presented. Initial test results on synthetic and real data are shown and discussed.