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Reconstructing geometric shapes from point clouds is a common task that is often accomplished by experts manually modeling geometries in CAD-capable software. State-of-the-art workflows based on fully automatic geometry extraction are limited by point cloud density and memory constraints, and require pre- and post-processing by the user. In this work, we present a framework for interactive, user-driven, feature-assisted geometry reconstruction from arbitrarily sized point clouds. Based on seeded region-growing point cloud segmentation, the user interactively extracts planar pieces of geometry and utilizes contextual suggestions to point out plane surfaces, normal and tangential directions, and edges and corners. We implement a set of feature-assisted tools for high-precision modeling tasks in architecture and urban surveying scenarios, enabling instant-feedback interactive point cloud manipulation on large-scale data collected from real-world building interiors and facades. We evaluate our results through systematic measurement of the reconstruction accuracy, and interviews with domain experts who deploy our framework in a commercial setting and give both structured and subjective feedback., 13 pages, submitted to Computers & Graphics Journal

MURFI was financially supported in part by the UK Space Agency, and by the following grants: Balme: UK SA grants ST/L00643X/1 and ST/R001413/1; Bridges: UK SA grant ST/R00143X/1; Butcher and Wright: STFC studentship grant ST/N50421X/1; Coates and Griffiths: UK SA grant ST/R002223/1; Cousins: Royal Society of Edinburgh Research Fellowship and UK SA grant ST/P001297/1; Davis: UK STFC grant ST/K502388/1; Grindrod: UK SA and STFC grants ST/J005215/1, ST/L006456/1, ST/N000528/1; Gunn: UK SA grants ST/P001408/1, ST/P001394/1, ST/N006410/1; Muller: UK SA grant ST/P002145/1; Preston: UK SA grant ST/P001254/1; Stabbins: UK SA studentship grant ST/N002377/1. We thank the UK Harwell Campus Satellite Applications Catapult for providing access to the control room that became the MOC, and the STFC Rutherford Appleton Laboratory for other ROC support. The 2016 Mars Utah Rover Field Investigation (MURFI) was a Mars rover field trial run by the UK Space Agency in association with the Canadian Space Agency's 2015/2016 Mars Sample Return Analogue Deployment mission. MURFI had over 50 participants from 15 different institutions around the UK and abroad. The objectives of MURFI were to develop experience and leadership within the UK in running future rover field trials; to prepare the UK planetary community for involvement in the European Space Agency/Roscosmos ExoMars 2020 rover mission; and to assess how ExoMars operations may differ from previous rover missions. Hence, the wider MURFI trial included a ten-day (or ten-‘sol’) ExoMars rover-like simulation. This comprised an operations team and control centre in the UK, and a rover platform in Utah, equipped with instruments to emulate the ExoMars rovers remote sensing and analytical suite. The operations team operated in ‘blind mode’, where the only available data came from the rover instruments, and daily tactical planning was performed under strict time constraints to simulate real communications windows. The designated science goal of the MURFI ExoMars rover-like simulation was to locate in-situ bedrock, at a site suitable for sub-surface core-sampling, in order to detect signs of ancient life. Prior to “landing”, the only information available to the operations team was Mars-equivalent satellite remote sensing data, which were used for both geologic and hazard (e.g., slopes, loose soil) characterisation of the area. During each sol of the mission, the operations team sent driving instructions and imaging/analysis targeting commands, which were then enacted by the field team and rover-controllers in Utah. During the ten-sol mission, the rover drove over 100 m and obtained hundreds of images and supporting observations, allowing the operations team to build up geologic hypotheses for the local area and select possible drilling locations. On sol 9, the team obtained a subsurface core sample that was then analyzed by the Raman spectrometer. Following the conclusion of the ExoMars-like component of MURFI, the operations and field team came together to evaluate the successes and failures of the mission, and discuss lessons learnt for ExoMars rover and future field trials. Key outcomes relevant to ExoMars rover included a key recognition of the importance of field trials for (i) understanding how to operate the ExoMars rover instruments as a suite, (ii) building an operations planning team that can work well together under strict time-limited pressure, (iii) developing new processes and workflows relevant to the ExoMars rover, (iv) understanding the limits and benefits of satellite mapping and (v) practicing efficient geological interpretation of outcrops and landscapes from rover-based data, by comparing the outcomes of the simulated mission with post-trial, in-situ field observations. In addition, MURFI was perceived by all who participated as a vital learning experience, especially for early and mid-career members of the team, and also demonstrated the UK capability of implementing a large rover field trial. The lessons learnt from MURFI are therefore relevant both to ExoMars rover, and to future rover field trials. Postprint

Testing for structural changes is a well studied field. Classical tests for breakpoint detection utilize F-statistics which depend on independent and identically normal distributed residuals. In general, this condition is not satisfied which leads to distorted test results when the p-values of classical tests are close to the significance level. Thus, permutation tests are used to properly estimate the critical values. Based on the accounting data of Austrian hospitals, collected by the Austrian social insurance institutions, specific observations (hospital stays) which are connected to pre-defined diseases are analysed. For those groups of observations we use characteristic factors, to test for structural changes from different perspectives. The first test analyses the temporal trend and identifies breakpoints, caused by changes in the underlying system between years. The second analysis focuses on identifying differences between hospitals. Both implemented tests ensure the often ignored aspect of a homogeneous data base for further analysis.