Reverse-engineering of architectural buildings based on an hybrid modeling approach

We thank MENSI and REALVIZ companies for their helpful comments and the following people for providing us images from their works: Francesca De Domenico (Fig. 1), Kyung-Tae Kim (Fig. 9). The CMN (French national center of patrimony buildings) is also acknowledged for the opportunity given to demonstrate our approach on the Hotel de Sully in Paris. We thank Tudor Driscu for his help on the English translation.This article presents a set of theoretical reflections and technical demonstrations that constitute a new methodological base for the architectural surveying and representation using computer graphics techniques. The problem we treated relates to three distinct concerns: the surveying of architectural objects, the construction and the semantic enrichment of their geometrical models, and their handling for the extraction of dimensional information. A hybrid approach to 3D reconstruction is described. This new approach combines range-based modeling and image-based modeling techniques; it integrates the concept of architectural feature-based modeling. To develop this concept set up a first process of extraction and formalization of architectural knowledge based on the analysis of architectural treaties is carried on. Then, the identified features are used to produce a template shape library. Finally the problem of the overall model structure and organization is addressed

A flexible and versatile studio for synchronized multi-view video recording

In recent years, the convergence of Computer Vision and Computer Graphics has put forth new research areas that work on scene reconstruction from and analysis of multi-view video footage. In free-viewpoint video, for example, new views of a scene are generated from an arbitrary viewpoint in real-time from a set of real multi-view input video streams. The analysis of real-world scenes from multi-view video to extract motion information or reflection models is another field of research that greatly benefits from high-quality input data. Building a recording setup for multi-view video involves a great effort on the hardware as well as the software side. The amount of image data to be processed is huge, a decent lighting and camera setup is essential for a naturalistic scene appearance and robust background subtraction, and the computing infrastructure has to enable real-time processing of the recorded material. This paper describes the recording setup for multi-view video acquisition that enables the synchronized recording of dynamic scenes from multiple camera positions under controlled conditions. The requirements to the room and their implementation in the separate components of the studio are described in detail. The efficiency and flexibility of the room is demonstrated on the basis of the results that we obtain with a real-time 3D scene reconstruction system, a system for non-intrusive optical motion capture and a model-based free-viewpoint video system for human actors.

Three dimensional pattern recognition using feature-based indexing and rule-based search

In flexible automated manufacturing, robots can perform routine operations as well as recover from atypical events, provided that process-relevant information is available to the robot controller. Real time vision is among the most versatile sensing tools, yet the reliability of machine-based scene interpretation can be questionable. The effort described here is focused on the development of machine-based vision methods to support autonomous nuclear fuel manufacturing operations in hot cells; This thesis presents a method to efficiently recognize 3D objects from 2D images based on feature-based indexing. Object recognition is the identification of correspondences between parts of a current scene and stored views of known objects, using chains of segments or indexing vectors. To create indexed object models, characteristic model image features are extracted during preprocessing. Feature vectors representing model object contours are acquired from several points of view around each object and stored. Recognition is the process of matching stored views with features or patterns detected in a test scene; Two sets of algorithms were developed, one for preprocessing and indexed database creation, and one for pattern searching and matching during recognition. At recognition time, those indexing vectors with the highest match probability are retrieved from the model image database, using a nearest neighbor search algorithm. The nearest neighbor search predicts the best possible match candidates. Extended searches are guided by a search strategy that employs knowledge-base (KB) selection criteria. The knowledge-based system simplifies the recognition process and minimizes the number of iterations and memory usage; Novel contributions include the use of a feature-based indexing data structure together with a knowledge base. Both components improve the efficiency of the recognition process by improved structuring of the database of object features and reducing data base size. This data base organization according to object features facilitates machine learning in the context of a knowledge-base driven recognition algorithm. Lastly, feature-based indexing permits the recognition of 3D objects based on a comparatively small number of stored views, further limiting the size of the feature database; Experiments with real images as well as synthetic images including occluded (partially visible) objects are presented. The experiments show almost perfect recognition with feature-based indexing, if the detected features in the test scene are viewed from the same angle as the view on which the model is based. The experiments also show that the knowledge base is a highly effective and efficient search tool recognition performance is improved without increasing the database size requirements. The experimental results indicate that feature-based indexing in combination with a knowledge-based system will be a useful methodology for automatic target recognition (ATR)

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