Self-correction of 3D reconstruction from multi-view stereo images

We present a self-correction approach to improving the 3D reconstruction of a multi-view 3D photogrammetry system. The self-correction approach has been able to repair the reconstructed 3D surface damaged by depth discontinuities. Due to self-occlusion, multi-view range images have to be acquired and integrated into a watertight nonredundant mesh model in order to cover the extended surface of an imaged object. The integrated surface often suffers from “dent” artifacts produced by depth discontinuities in the multi-view range images. In this paper we propose a novel approach to correcting the 3D integrated surface such that the dent artifacts can be repaired automatically. We show examples of 3D reconstruction to demonstrate the improvement that can be achieved by the self-correction approach. This self-correction approach can be extended to integrate range images obtained from alternative range capture devices

Computer Vision and Graphics for Heritage Preservation and Digital Archaeology

The goal of this work is to provide attendees with a survey of topics related to Heritage Preservation and Digital Archeology, which are challenging and motivating subjects to both computer vision and graphics community. These issues have been gaining increasing attention and priority within the scientific scenario and among funding agencies and development organizations over the last years. Motivations to this work are the recent efforts in the digital preservation of cultural heritage objects and sites before degradation or damage caused by environmental factors or human development. One of the main focuses of these researches is the development of new techniques for realistic 3D model building from images, preserving as much information as possible. We intend to introduce and discuss several emerging topics in computer vision and graphics related to the proposed theme while highlighting the major contributions and advances in these fields

Integration of range images in a multi-view stereo system

A novel method for integrating multiple range images in a multi-view stereo imaging system is presented here. Due to self-occlusion an individual range image provides only a partial model of an object surface. Therefore multiple range images from differing viewpoints must be captured and merged to extend the surface area that can be captured. In our approach range images are decomposed into subset patches and then evaluated in a "confidence competition". Redundant patches are removed whilst winning patches are merged to complete a single plausible mesh that represents the acquired object surface

A geometrical-based approach to recognise structure of complex interiors

3D modelling of building interiors has gained a lot of interest recently, specifically since the rise of Building Information Modeling (BIM). A number of methods have been developed in the past, however most of them are limited to modelling non-complex interiors. 3D laser scanners are the preferred sensor to collect the 3D data, however the cost of state-of-the-art laser scanners are prohibitive to many. Other types of sensors could also be used to generate the 3D data but they have limitations especially when dealing with clutter and occlusions. This research has developed a platform to produce 3D modelling of building interiors while adapting a low-cost, low-level laser scanner to generate the 3D interior data. The PreSuRe algorithm developed here, which introduces a new pipeline in modelling building interiors, combines both novel methods and adapts existing approaches to produce the 3D modelling of various interiors, from sparse room to complex interiors with non-ideal geometrical structure, highly cluttered and occluded. This approach has successfully reconstructed the structure of interiors, with above 96% accuracy, even with high amount of noise data and clutter. The time taken to produce the resulting model is almost real-time, compared to existing techniques which may take hours to generate the reconstruction. The produced model is also equipped with semantic information which differentiates the model from a regular 3D CAD drawing and can be use to assist professionals and experts in related fields

3D Data Processing: Towards the Automated BIM in Inhabited Indoors

[EN] In this paper we present a method for the reconstruction of interiors using a set of panoramic range data in scenes with clutter and occlusion. Although the ultimate goal of our project is to obtain automated Building Information Models of facilities, here we specifically deal with the reconstruction of simply-shaped wide areas (such as walls, ceilings and floors) behind furniture and facility pieces in interiors. Our approach is based on a sequential updating labeling strategy in different data representation spaces. A volumetric representation is used to permit the labeling of the 3D space for different range data and the fusion of all the scene’s labels to obtain one single 2D labeling image for each of the simply-shaped wide areas of the room. Based on this labeling process, our method is able both to identify the occluded regions in the wall and, through a learning technique, to infer essential parts, such as doors, windows and closets. This method was tested under difficult clutter and occlusion conditions, yielding promising results. Scans were obtained using a state of the art laser scanner operated by a professional 3D scanning service provider.[ES] Hasta la fecha, el procesamiento de la información proporcionada por escáners de media distancia en entornos de construcciones civiles y edificios se ha limitado, en la mayoría de los casos, a tareas de registro o generación manual de modelos tridimensionales CAD. En este artículo se da un paso más allá, acometiendo soluciones para problemas de segmentación automática y reconocimiento de partes representativas del entorno como uno de los pasos esenciales hacia la generación automática de modelos BIM (Building Information Models) en entornos habitados. Específicamente, se propone un procedimiento para identificar partes esenciales de la estructura de interiores de edificios en entornos altamente desordenados y con un alto componente de oclusión (Figura 1). La dificultad en el tratamiento de millones de puntos inconexos en un espacio no estructurado con fines de inteligencia artificial, hace especialmente atractiva esta línea de investigación, aun no desarrollada en la comunidad científica. El artículo expone una solución a través de etiquetado dinámico y aprendizaje en varias fases que finaliza con la reconstrucción de la superficie básica de interiores (paredes, suelo y techo) y la identificación de partes importantes en el modelo BIM en interiores (puertas, ventanas, armarios, etc). La técnica presentada en este artículo se ha experimentado con éxito sobre datos 3D de edificios proporcionados por empresas profesionales en digitalización con láser escáners de media distancia.Este trabajo ha sido realizado bajo financiación del National Science Foundation (NSF) de Estados Unidos en el proyecto “Automating the Creation of As-built Building Information Models” CMMI-0856558 , desarrollado en la Universidad de Carnegie Mellon, Pittsburgh, PA, USA. "Adán, A.; Huber, D. (2011). Análisis de Datos 3D Para Generación Automática de Modelos BIM de Interiores Habitados. Revista Iberoamericana de Automática e Informática industrial. 8(4):357-370. https://doi.org/10.1016/j.riai.2011.09.010OJS35737084Dell’Acqua, F., & Fisher, R. (2002). Reconstruction of planar surfaces behind occlusions in range images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(4), 569-575. doi:10.1109/34.993564Elmqvist, N., & Tsigas, P. (2008). A Taxonomy of 3D Occlusion Management for Visualization. IEEE Transactions on Visualization and Computer Graphics, 14(5), 1095-1109. doi:10.1109/tvcg.2008.59Han, F., Tu, Z., & Zhu, S.-C. (2002). A Stochastic Algorithm for 3D Scene Segmentation and Reconstruction. Lecture Notes in Computer Science, 502-516. doi:10.1007/3-540-47977-5_33Muñoz-Salinas, R., Aguirre, E., & García-Silvente, M. (2006). Detection of doors using a genetic visual fuzzy system for mobile robots. Autonomous Robots, 21(2), 123-141. doi:10.1007/s10514-006-7847-8Wang, J., & Oliveira, M. M. (2002). Improved Scene Reconstruction from Range Images. Computer Graphics Forum, 21(3), 521-530. doi:10.1111/1467-8659.0070