AUTOMATED DETECTION AND LAYOUT REGULARIZATION OF SIMILAR FEATURES IN INDOOR POINT CLOUD

Automated identification of high-level structures in unorganized point cloud of indoor spaces Indoor space is an important aspect of scene analysis that provides essential information for many applications, such as building digitization, indoor navigation and evacuation route planning. In addition, detection of repetition and regularities in the organization indoor environments, such as rooms, can be used to provide a contextual relationship in the reconstruction phase. However, retrieving high-level information is a challenging task due to the unorganized nature of the raw data, poor-quality of the input data that are in many cases contaminated with noise and outliers. in point benefit from the apparent regularities and strong contextual relationships in façades. The main observation exploited in this paper is the fact that building indoor is generally constituted by a set of basic shapes repeated several times in regular layouts. Building elements can be considered as similar if they share a set of features and elements in an idealized layout exhibiting some regularities. Starting from this main assumption a recursive adaptive partitioning of the indoor point cloud is carried out to automatically derive a flexible and hierarchical 3D representation of the building space. The presented methodology is tested on a synthetic dataset with Gaussian noise. The reconstructed pattern shows a close correspondence with the synthetic one showing the viability of the proposed approach

Learning Grammars for Architecture-Specific Facade Parsing

International audienceParsing facade images requires optimal handcrafted grammar for a given class of buildings. Such a handcrafted grammar is often designed manually by experts. In this paper, we present a novel framework to learn a compact grammar from a set of ground-truth images. To this end, parse trees of ground-truth annotated images are obtained running existing inference algorithms with a simple, very general grammar. From these parse trees, repeated subtrees are sought and merged together to share derivations and produce a grammar with fewer rules. Furthermore, unsupervised clustering is performed on these rules, so that, rules corresponding to the same complex pattern are grouped together leading to a rich compact grammar. Experimental validation and comparison with the state-of-the-art grammar-based methods on four diff erent datasets show that the learned grammar helps in much faster convergence while producing equal or more accurate parsing results compared to handcrafted grammars as well as grammars learned by other methods. Besides, we release a new dataset of facade images from Paris following the Art-deco style and demonstrate the general applicability and extreme potential of the proposed framework

Automatic generation of tourist maps

Tourist maps are essential resources for visitors to an unfamiliar city because they visually highlight landmarks and other points of interest. Yet, hand-designed maps are static representations that cannot adapt to the needs and tastes of the individual tourist. In this paper we present an automated system for designing tourist maps that selects and highlights the information that is most important to tourists. Our system determines the salience of map elements using bottom-up vision-based image analysis and top-down web-based information extraction techniques. It then generates a map that emphasizes the most important elements, using a combination of multiperspective rendering to increase visibility of streets and landmarks, and cartographic generalization techniques such as simplification, deformation, and displacement to emphasize landmarks and de-emphasize less important buildings. We show a number of automatically generated tourist maps of San Francisco and compare them to existing automated and manual approaches. © 2008 ACM

Holistic interpretation of visual data based on topology:semantic segmentation of architectural facades

The work presented in this dissertation is a step towards effectively incorporating contextual knowledge in the task of semantic segmentation. To date, the use of context has been confined to the genre of the scene with a few exceptions in the field. Research has been directed towards enhancing appearance descriptors. While this is unarguably important, recent studies show that computer vision has reached a near-human level of performance in relying on these descriptors when objects have stable distinctive surface properties and in proper imaging conditions. When these conditions are not met, humans exploit their knowledge about the intrinsic geometric layout of the scene to make local decisions. Computer vision lags behind when it comes to this asset. For this reason, we aim to bridge the gap by presenting algorithms for semantic segmentation of building facades making use of scene topological aspects. We provide a classification scheme to carry out segmentation and recognition simultaneously.The algorithm is able to solve a single optimization function and yield a semantic interpretation of facades, relying on the modeling power of probabilistic graphs and efficient discrete combinatorial optimization tools. We tackle the same problem of semantic facade segmentation with the neural network approach.We attain accuracy figures that are on-par with the state-of-the-art in a fully automated pipeline.Starting from pixelwise classifications obtained via Convolutional Neural Networks (CNN). These are then structurally validated through a cascade of Restricted Boltzmann Machines (RBM) and Multi-Layer Perceptron (MLP) that regenerates the most likely layout. In the domain of architectural modeling, there is geometric multi-model fitting. We introduce a novel guided sampling algorithm based on Minimum Spanning Trees (MST), which surpasses other propagation techniques in terms of robustness to noise. We make a number of additional contributions such as measure of model deviation which captures variations among fitted models