An evaluation framework for benchmarking indoor modelling methods

Despite recent progress in the development of methods for automated reconstruction of indoor models, a comparative performance evaluation of these methods is not available due to the lack of publicly available benchmark datasets and a common evaluation framework. The ISPRS Benchmark on Indoor Modelling is an effort to enable comparison and benchmarking of indoor modelling methods by providing a benchmark dataset and a comprehensive evaluation framework. In this paper, we propose a framework for the evaluation of indoor modelling methods, and discuss various quality aspects of the reconstruction methods as well as the reconstructed models. We discuss the challenges in quantitative quality evaluation of indoor models through comparison with a reference model, and propose suitable measures and methods for comparing an automatically reconstructed indoor model with a reference.Xunta de Galicia | Ref. ED481B 2016/079-

State of research in automatic as-built modelling

This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.aei.2015.01.001Building Information Models (BIMs) are becoming the official standard in the construction industry for encoding, reusing, and exchanging information about structural assets. Automatically generating such representations for existing assets stirs up the interest of various industrial, academic, and governmental parties, as it is expected to have a high economic impact. The purpose of this paper is to provide a general overview of the as-built modelling process, with focus on the geometric modelling side. Relevant works from the Computer Vision, Geometry Processing, and Civil Engineering communities are presented and compared in terms of their potential to lead to automatic as-built modelling.We acknowledge the support of EPSRC Grant NMZJ/114,DARPA UPSIDE Grant A13–0895-S002, NSF CAREER Grant N. 1054127, European Grant Agreements No. 247586 and 334241. We would also like to thank NSERC Canada, Aecon, and SNC-Lavalin for financially supporting some parts of this research

Void-growing: a novel Scan-to-BIM method for manhattan world buildings from point cloud

The automated generation of 3D models of buildings from point clouds is still under heavy research. Currently, this Scan-to-BIM process requires high manual effort, and the previous research focuses on simple room structure under low occlusion level. We propose a novel “void-growing” approach that extracts walls, floors, and ceilings automatically. Different from the majority of current approaches starting with detecting surfaces of elements in point clouds, our approach grows the void volume space inside a room first. Our approach performs well in occluded environments. It can reconstruct simple cuboid rooms as well as complex rooms like L-shape and U-shape rooms. Different ceiling heights caused by suspended ceilings can also be represented properly.TUM-IAS Hans Fisher Senior Fellowshi

High-Throughput System for the Early Quantification of Major Architectural Traits in Olive Breeding Trials Using UAV Images and OBIA Techniques

The need for the olive farm modernization have encouraged the research of more efficient crop management strategies through cross-breeding programs to release new olive cultivars more suitable for mechanization and use in intensive orchards, with high quality production and resistance to biotic and abiotic stresses. The advancement of breeding programs are hampered by the lack of efficient phenotyping methods to quickly and accurately acquire crop traits such as morphological attributes (tree vigor and vegetative growth habits), which are key to identify desirable genotypes as early as possible. In this context, an UAV-based high-throughput system for olive breeding program applications was developed to extract tree traits in large-scale phenotyping studies under field conditions. The system consisted of UAV-flight configurations, in terms of flight altitude and image overlaps, and a novel, automatic, and accurate object-based image analysis (OBIA) algorithm based on point clouds, which was evaluated in two experimental trials in the framework of a table olive breeding program, with the aim to determine the earliest date for suitable quantifying of tree architectural traits. Two training systems (intensive and hedgerow) were evaluated at two very early stages of tree growth: 15 and 27 months after planting. Digital Terrain Models (DTMs) were automatically and accurately generated by the algorithm as well as every olive tree identified, independently of the training system and tree age. The architectural traits, specially tree height and crown area, were estimated with high accuracy in the second flight campaign, i.e. 27 months after planting. Differences in the quality of 3D crown reconstruction were found for the growth patterns derived from each training system. These key phenotyping traits could be used in several olive breeding programs, as well as to address some agronomical goals. In addition, this system is cost and time optimized, so that requested architectural traits could be provided in the same day as UAV flights. This high-throughput system may solve the actual bottleneck of plant phenotyping of "linking genotype and phenotype," considered a major challenge for crop research in the 21st century, and bring forward the crucial time of decision making for breeders