CSGNet: Neural Shape Parser for Constructive Solid Geometry

We present a neural architecture that takes as input a 2D or 3D shape and outputs a program that generates the shape. The instructions in our program are based on constructive solid geometry principles, i.e., a set of boolean operations on shape primitives defined recursively. Bottom-up techniques for this shape parsing task rely on primitive detection and are inherently slow since the search space over possible primitive combinations is large. In contrast, our model uses a recurrent neural network that parses the input shape in a top-down manner, which is significantly faster and yields a compact and easy-to-interpret sequence of modeling instructions. Our model is also more effective as a shape detector compared to existing state-of-the-art detection techniques. We finally demonstrate that our network can be trained on novel datasets without ground-truth program annotations through policy gradient techniques.Comment: Accepted at CVPR-201

Creation and Spatial Analysis of 3D City Modeling based on GIS Data

The 3D city model is one of the crucial topics that are still under analysis by many engineers and programmers because of the great advancements in data acquisition technologies and 3D computer graphics programming. It is one of the best visualization methods for representing reality. This paper presents different techniques for the creation and spatial analysis of 3D city modeling based on Geographical Information System (GIS) technology using free data sources. To achieve that goal, the Mansoura University campus, located in Mansoura city, Egypt, was chosen as a case study. The minimum data requirements to generate a 3D city model are the terrain, 2D spatial features such as buildings, landscape area and street networks. Moreover, building height is an important attribute in the 3D extrusion process. The main challenge during the creation process is the dearth of accurate free datasets, and the time-consuming editing. Therefore, different data sources are used in this study to evaluate their accuracy and find suitable applications which can use the generated 3D model. Meanwhile, an accurate data source obtained using the traditional survey methods is used for the validation purpose. First, the terrain was obtained from a digital elevation model (DEM) and compared with grid leveling measurements. Second, 2D data were obtained from: the manual digitization from (30 cm) high-resolution imagery, and deep learning structure algorithms to detect the 2D features automatically using an object instance segmentation model and compared the results with the total station survey observations. Different techniques are used to investigate and evaluate the accuracy of these data sources. The procedural modeling technique is applied to generate the 3D city model. TensorFlow & Keras frameworks (Python APIs) were used in this paper; moreover, global mapper, ArcGIS Pro, QGIS and CityEngine software were used. The precision metrics from the trained deep learning model were 0.78 for buildings, 0.62 for streets and 0.89 for landscape areas. Despite, the manual digitizing results are better than the results from deep learning, but the extracted features accuracy is accepted and can be used in the creation process in the cases not require a highly accurate 3D model. The flood impact scenario is simulated as an application of spatial analysis on the generated 3D city model. Doi: 10.28991/CEJ-2022-08-01-08 Full Text: PD

Formalization and automatic interpretation of map requirements

The map requirements (constraints) can be interpreted by computer programs using their basic embedded functionalities. There are a huge number of constraints available to define the objective of various generalization outputs. Some of the constraints contain high-level knowledge which is not easy to interpret. This needs a huge amount of efforts to implement those constraints. The fact that many constraints have something in common makes the implementation per constraint a waste of resource. The paper proposes to decompose the constraints into more basic units, so as to interpret those constraints more flexible and reuse the already developed functionality as much as possible

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

Effects of picture prompts delivered by a video iPodRTM on pedestrian navigation

National data continue to indicate many individuals with intellectual and developmental disabilities (IDD) have not had the same access to education, employment, independent living, or extracurricular activities as the general population after high school (Blackorby & Wagner, 1996; Newman, Wagner, Cameto, & Knokey, 2009; Wagner, Newman, Cameto, Garza, & Levine, 2005; Wagner, Newman, Cameto, Levine, & Garza, 2006). Transportation access can be a major contributor to independence, productivity, and societal inclusion for individuals with disabilities (Myers, 1996). Individuals with IDD face many challenges related to community integration such as obstacles to independently navigate in the community (Sohlberg, Fickas, Lemoncello, & Hung, 2009). Travel training and pedestrian navigation skills are critical since these skills impact how people live, work, and participate in their community (Groce, 1996b). It is important to have an organized and sequential way to teach independent travel to individuals with IDD since most do not learn these skills incidentally or obtain a driver’s license to navigate independently (LaGrow, Wiener, & LaDuke, 1990). This study examined the effects of pedestrian navigation training using picture prompts displayed through a video iPod® on travel route completion with four young adults with IDD (18-26 years old) participating in an inclusive individualized postsecondary program at a 4-year university. Results indicated a functional relation between picture prompts displayed on the video iPod® and participants’ acquisition of pedestrian navigation skills to and from various campus locations. Maintenance data indicated all four participants were able to continue to navigate trained routes independently for up to 28 days using the video iPod®. Generalization measures indicated 3 out of 4 participants were able to use the video iPod® to navigate untrained routes without any prompts given by the researcher. Social validity data suggested iPod® training and supports were useful and practical for teaching independent pedestrian navigation skills. Finally, limitations, suggestions for future research, and implications for practice were provided

From Raw Data to Meaningful Information: A Representational Approach to Cadastral Databases in Relation to Urban Planning

Digesting the data hose that cities are constantly producing is complex; data is usually structured with different criteria, which makes comparative analysis of multiple cities challenging. However, the publicly available data from the Spanish cadaster contains urban information in a documented format with common semantics for the whole territory, which makes these analyses possible. This paper uses the information about the 3D geometry of buildings, their use and their year of construction, stored in cadastral databases, to study the relation between the built environment (what the city is) and the urban plan (what the city wants to become), translating the concepts of the cadastral data into the semantics of the urban plan. Different representation techniques to better understand the city from the pedestrians’ point of view and to communicate this information more effectively are also discussed.Postprint (published version