Sketch-a-Net that Beats Humans

We propose a multi-scale multi-channel deep neural network framework that, for the first time, yields sketch recognition performance surpassing that of humans. Our superior performance is a result of explicitly embedding the unique characteristics of sketches in our model: (i) a network architecture designed for sketch rather than natural photo statistics, (ii) a multi-channel generalisation that encodes sequential ordering in the sketching process, and (iii) a multi-scale network ensemble with joint Bayesian fusion that accounts for the different levels of abstraction exhibited in free-hand sketches. We show that state-of-the-art deep networks specifically engineered for photos of natural objects fail to perform well on sketch recognition, regardless whether they are trained using photo or sketch. Our network on the other hand not only delivers the best performance on the largest human sketch dataset to date, but also is small in size making efficient training possible using just CPUs.Comment: Accepted to BMVC 2015 (oral

Consistency constraints and 3D building reconstruction

International audienceVirtual architectural (indoor) scenes are often modeled in 3D for various types of simulation systems. For instance, some authors propose methods dedicated to lighting, heat transfer, acoustic or radio-wave propagation simulations. These methods rely in most cases on a volumetric representation of the environment, with adjacency and incidence relationships. Unfortunately, many buildings data are only given by 2D plans and the 3D needs varies from one application to another. To face these problems, we propose a formal representation of consistency constraints dedicated to building interiors and associated with a topological model. We show that such a representation can be used for: (i) reconstructing 3D models from 2D architectural plans (ii) detecting automatically geometrical, topological and semantical inconsistencies (iii) designing automatic and semi-automatic operations to correct and enrich a 2D plan. All our constraints are homogeneously defined in 2D and 3D, implemented with generalized maps and used in modeling operations. We explain how this model can be successfully used for lighting and radio-wave propagation simulations

DIGITAL SECURITY: 3D GEOMETRY PROTECTION OF THE AUTOMATICALLY RESTITUTED HISTORICAL BUILDINGS

This paper describes a novel method of data protection of the three-dimensional (3D) models that are obtained from automatic process of geometric restitution, using old two-dimensional (2D) architectural and artistic drawings. The first contribution of our research is the algorithm that includes several image processing steps, which are required in order to define walls, staircases and openings from the digitalized hand drawn architectural plans. The result of this step is detailed 3D model of the digitally processed historical building plans. The experimental confirmation of the algorithm accuracy is 3D model of the Chateau de Versailles, which is descripted by old hand drawings, dating between the end of the XVII and the XIX century. Next part of our research is theoretical and mathematical analysis of geometrical features of such 3D model that is a result of the image processing algorithm. The key-achievement of this part is new method of protecting the geometrical data using optimized adaptive Sparse Quantization Index Modulation (QIM) for embedding data bits into essential structure of the generated model. As a final result we present a secure authentication of the automatically restituted 3D model of the historically important artifact

Representation Challenges

Augmented Reality (AR) and Artificial Intelligence (AI) are technological domains that closely interact with space at architectural and urban scale in the broader ambits of cultural heritage and innovative design. The growing interest is perceivable in many fields of knowledge, supported by the rapid development and advancement of theory and application, software and devices, fueling a pervasive phenomenon within our daily lives. These technologies demonstrate to be best exploited when their application and other information and communication technology (ICT) advancements achieve a continuum. In particular, AR defines an alternative path to observe, analyze and communicate space and artifacts. Besides, AI opens future scenarios in data processing, redefining the relationship between man and computer. In the last few years, the AR/AI expansion and relationship have raised deep transdisciplinary speculation. The research experiences have shown many cross-relations in Architecture and Design domains. Representation studies could arise an international debate as a convergence place of multidisciplinary theoretical and applicative contributions related to architecture, city, environment, tangible and intangible Cultural Heritage. This book collects 66 papers and identify eight lines of research that may guide future developments