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This paper addresses the structure-and-motion problem, that requires to find camera motion and 3D structure 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.

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ABSTRACT:This paper addresses the problem of 3D building reconstruction from thermal infrared (TIR) images. We show that a commercial Computer Vision software can be used to automatically orient sequences of TIR images taken from an Unmanned Aerial Vehicle (UAV) and to generate 3D point clouds, without requiring any GNSS/INS data about position and attitude of the images nor camera calibration parameters. Moreover, we propose a procedure based on Iterative Closest Point (ICP) algorithm to create a model that combines high resolution and geometric accuracy of RGB images with the thermal information deriving from TIR images. The process can be carried out entirely by the aforesaid software in a simple and efficient way.

ABSTRACT:A novel multi-view stereo reconstruction method is presented. The algorithm is focused on accuracy and it is highly engineered with some parts taking advantage of the graphics processing unit. In addition, it is seamlessly integrated with the output of a structure and motion pipeline. In the first part of the algorithm a depth map is extracted independently for each image. The final depth map is generated from the depth hypothesis using a Markov random field optimization technique over the image grid. An octree data structure accumulates the votes coming from each depth map. A novel procedure to remove rogue points is proposed that takes into account the visibility information and the matching score of each point. Finally a texture map is built by wisely making use of both the visibility and the view angle informations. Several results show the effectiveness of the algorithm under different working scenarios.

Going from unstructured cloud of points to surfaces is a challenging problem. However, as points are produced by a structure-and-motion pipeline, image-consistency is a powerful clue that comes to the rescue. In this paper we present a method for extracting planar patches from an unstructured cloud of points, based on the detection of image-consistent planar patches with J-linkage, a robust algorithm for multiple models fitting. The method integrates several constraints inside J-linkage, optimizes the position of the points with regard to image-consistency and deploys a hierarchical processing scheme that decreases the computational load. With respect to previous work this approach has the advantage of starting from sparse data. Several results show the e↵ectiveness of the proposed approach.

Planar patches are a very compact and stable intermediate representation of 3D scenes, as they are a good starting point for a complete automatic reconstruction of surfaces. This paper presents a novel method for extracting planar patches from an unstructured cloud of points that is produced by a typical structure and motion pipeline. The method integrates several constraints inside J-linkage, a robust algorithm for multiple models fitting. It makes use of information coming both from the 3D structure and the images. Several results show the effectiveness of the proposed approach.