Scalable Surface Reconstruction from Point Clouds with Extreme Scale and Density Diversity

In this paper we present a scalable approach for robustly computing a 3D surface mesh from multi-scale multi-view stereo point clouds that can handle extreme jumps of point density (in our experiments three orders of magnitude). The backbone of our approach is a combination of octree data partitioning, local Delaunay tetrahedralization and graph cut optimization. Graph cut optimization is used twice, once to extract surface hypotheses from local Delaunay tetrahedralizations and once to merge overlapping surface hypotheses even when the local tetrahedralizations do not share the same topology.This formulation allows us to obtain a constant memory consumption per sub-problem while at the same time retaining the density independent interpolation properties of the Delaunay-based optimization. On multiple public datasets, we demonstrate that our approach is highly competitive with the state-of-the-art in terms of accuracy, completeness and outlier resilience. Further, we demonstrate the multi-scale potential of our approach by processing a newly recorded dataset with 2 billion points and a point density variation of more than four orders of magnitude - requiring less than 9GB of RAM per process.Comment: This paper was accepted to the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. The copyright was transfered to IEEE (ieee.org). The official version of the paper will be made available on IEEE Xplore (R) (ieeexplore.ieee.org). This version of the paper also contains the supplementary material, which will not appear IEEE Xplore (R

Graz Griffins’ Solution to the European Robotics Challenges 2014

An important focus of current research in the field of Micro Aerial Vehicles (MAVs) is to increase the safety of their operation in general unstructured environments. An example of a real-world application is visual inspection of industry infrastructure, which can be greatly facilitated by autonomous multicopters. Currently, active research is pursued to improve real-time vision-based localization and navigation algorithms. In this context, the goal of Challenge 3 of the EuRoC 20144 Simulation Contest was a fair comparison of algorithms in a realistic setup which also respected the computational restrictions onboard an MAV. The evaluation separated the problem of autonomous navigation into four tasks: visual-inertial localization, visual-inertial mapping, control and state estimation, and trajectory planning. This EuRoC challenge attracted the participation of 21 important European institutions. This paper describes the solution of our team, the Graz Griffins, to all tasks of the challenge and presents the achieved results

Evaluations on multi-scale camera networks for precise and geo-accurate reconstructions from aerial and terrestrial images with user guidance

During the last decades photogrammetric computer vision systems have been well established in scien- tific and commercial applications. Recent developments in image-based 3D reconstruction systems have resulted in an easy way of creating realistic, visually appealing and accurate 3D models. We present a fully automated processing pipeline for metric and geo-accurate 3D reconstructions of complex geome- tries supported by an online feedback method for user guidance during image acquisition. Our approach is suited for seamlessly matching and integrating images with different scales, from different view points (aerial and terrestrial), and with different cameras into one single reconstruction. We evaluate our ap- proach based on different datasets for applications in mining, archaeology and urban environments and thus demonstrate the flexibility and high accuracy of our approach. Our evaluation includes accuracy related analyses investigating camera self-calibration, georegistration and camera network configuration