Generating a Visual Language of Performance-Driven Configurations for the Principal Façade of a Prototype Sustainable House

A performance-driven application of shape grammars is presented. A parametric shape grammar that generates a language of pattern designs for the principal façade of a prototype house—featuring a 5 × 20 matrix of electrochromic windows—based equally on performance and aesthetic criteria is described. The adjustment of the chromatism and light transmittance of each individual windowpane on the façade enables the adjustment of solar radiation at the house interior. The novel aspect of the grammar is that it encodes performance constraints of interior daylight illuminance and associates them to visual, symmetry principles of two-dimensional pattern generation. Twelve parametric rules account for the generation of the façade pattern language and five subclasses account for the symmetry of the patterns in the language.     

Style grammars for interactive visualization of architecture

Interactive visualization of architecture provides a way to quickly visualize existing or novel buildings and structures. Such applications require both fast rendering and an effortless input regimen for creating and changing architecture using high-level editing operations that automatically fill in the necessary details. Procedural modeling and synthesis is a powerful paradigm that yields high data amplification and can be coupled with fast-rendering techniques to quickly generate plausible details of a scene without much or any user interaction. Previously, forward generating procedural methods have been proposed where a procedure is explicitly created to generate particular content. In this paper, we present our work in inverse procedural modeling of buildings and describe how to use an extracted repertoire of building grammars to facilitate the visualization and quick modification of architectural structures and buildings. We demonstrate an interactive application where the user draws simple building blocks and, using our system, can automatically complete the building "in the style of other buildings using view-dependent texture mapping or nonphotorealistic rendering techniques. Our system supports an arbitrary number of building grammars created from user subdivided building models and captured photographs. Using only edit, copy, and paste metaphors, the entire building styles can be altered and transferred from one building to another in a few operations, enhancing the ability to modify an existing architectural structure or to visualize a novel building in the style of the others.     

Automatic generation of fabrication drawings for façade mullions and transoms through BIM models

Fabrication drawings are essential for manufacturing, design evaluation and inspection of building components, especially for building façade structural components. In order to clearly represent the physical characteristics of the façade structural components, a large number of section views need to be produced, which is very time-consuming and labor intensive. Therefore, automatic generation of fabrication drawings for building façade components (such as mullions and transoms) is of paramount importance. In this paper, attempts have been made to develop an efficient framework in order to automatically generate fabrication drawings for building façade structural components, including mullions and transoms. To represent the complex physical characteristics (such as holes and notches) on mullions and transoms using minimum number of drawing views, a computational algorithm based on graph theory is developed to eliminate duplicated section views. Another methodology regarding the generation of breaks for top views is also proposed to further improve the quality of drawing layouts. The obtained drawing views are then automatically arranged using a developed approach. In addition, primary dimensions of the drawing views focusing on the physical features are also generated. Furthermore, in order to maintain the consistency of drawing formats across multiple drawings, a methodology is proposed to determine the scaling factors of the drawings by using clustering technique. In an illustrative example, the proposed framework is used to generate the fabrication drawings for a typical BIM model containing façade structural components, and saving in time is observed.     

Modular grammar specification

We establish a semantics for building grammars from a modularised specification in which modules are able to delete productions from imported nonterminals. Modules have import lists of nonterminals; some or all of an imported nonterminal's productions may be suppressed at import time. There are two basic import mechanisms which (a) reference or (b) clone an imported nonterminal's productions. One of our goals is to allow a precise answer to the question: ‘what character level language does this grammar generate’ in the face of difficult issues such as the mutual embedding of languages that have different whitespace and commenting conventions. Our technique is to automatically generate a character level grammar from grammars written at token level in the conventional way; the grammar is constructed from modules each of which may have its own whitespace convention.     

Design Transformations for Rule‐based Procedural Modeling

We introduce design transformations for rule‐based procedural models, e.g., for buildings and plants. Given two or more procedural designs, each specified by a grammar, a design transformation combines elements of the existing designs to generate new designs. We introduce two technical components to enable design transformations. First, we extend the concept of discrete rule switching to rule merging, leading to a very large shape space for combining procedural models. Second, we propose an algorithm to jointly derive two or more grammars, called grammar co‐derivation. We demonstrate two applications of our work: we show that our framework leads to a larger variety of models than previous work, and we show fine‐grained transformation sequences between two procedural models.     

Automatic modeling of cluttered multi‐room floor plans from panoramic images

We present a novel and light‐weight approach to capture and reconstruct structured 3D models of multi‐room floor plans. Starting from a small set of registered panoramic images, we automatically generate a 3D layout of the rooms and of all the main objects inside. Such a 3D layout is directly suitable for use in a number of real‐world applications, such as guidance, location, routing, or content creation for security and energy management. Our novel pipeline introduces several contributions to indoor reconstruction from purely visual data. In particular, we automatically partition panoramic images in a connectivity graph, according to the visual layout of the rooms, and exploit this graph to support object recovery and rooms boundaries extraction. Moreover, we introduce a plane‐sweeping approach to jointly reason about the content of multiple images and solve the problem of object inference in a top‐down 2D domain. Finally, we combine these methods in a fully automated pipeline for creating a structured 3D model of a multi‐room floor plan and of the location and extent of clutter objects. These contribution make our pipeline able to handle cluttered scenes with complex geometry that are challenging to existing techniques. The effectiveness and performance of our approach is evaluated on both real‐world and synthetic models.     

Finding Syntactic Structures from Human Motion Data

We present a new approach to motion rearrangement that preserves the syntactic structures of an input motion automatically by learning a context‐free grammar from the motion data. For grammatical analysis, we reduce an input motion into a string of terminal symbols by segmenting the motion into a series of subsequences, and then associating a group of similar subsequences with the same symbol. To obtain the most repetitive and precise set of terminals, we search for an optimial segmentation such that a large number of subsequences can be clustered into groups with little error. Once the input motion has been encoded as a string, a grammar induction algorithm is employed to build up a context‐free grammar so that the grammar can reconstruct the original string accurately as well as generate novel strings sharing their syntactic structures with the original string. Given any new strings from the learned grammar, it is straightforward to synthesize motion sequences by replacing each terminal symbol with its associated motion segment, and stitching every motion segment sequentially. We demonstrate the usefulness and flexibility of our approach by learning grammars from a large diversity of human motions, and reproducing their syntactic structures in new motion sequences.     

Automatic façade recovery from single nighttime image

Nighttime images are difficult to process due to insufficient brightness,lots of noise,and lack of details.Therefore,they are always removed from time-lapsed image analysis.It is interesting that nighttime images have a unique and wonderful building features that have robust and salient lighting cues from human activities.Lighting variation depicts both the statistical and individual habitation,and it has an inherent man-made repetitive structure from architectural theory.Inspired by this,we propose an automatic nighttime fa?ade recovery method that exploits the lattice structures of window lighting.First,a simple but efficient classification method is employed to determine the salient bright regions,which may be lit windows.Then we groupwindows into multiple lattice proposals with respect to fa?ades by patch matching,followed by greedily removing overlapping lattices.Using the horizon constraint,we solve the ambiguous proposals problem and obtain the correct orientation.Finally,we complete the generated fa?ades by filling in the missing windows.This method is well suited for use in urban environments,and the results can be used as a good single-view compensation method for daytime images.The method also acts as a semantic input to other learning-based 3D image reconstruction techniques.The experiment demonstrates that our method works well in nighttime image datasets,and we obtain a high lattice detection rate of 82.1%of 82 challenging images with a low mean orientation error of 12.1±4.5 degrees.     

Drawing Syntax Diagrams in Haskell

The construction of flexible tools is a promising area for applications of functional programming. The educational tool Ebnf2ps is a medium-scale real world program that translates grammars into syntax diagrams. Its distinguishing feature is its grammar transformation mechanism, which generates highly readable output even from LALR grammars. It is therefore applicable to automatically generate documentation for the input language of parsers developed with popular parser generators, such as yacc, bison, and Happy. Ebnf2ps owes its existence–specifically the transformation feature–to its implementation language, Haskell. ©1997 by John Wiley & Sons, Ltd     

Design with shape grammars and reinforcement learning

Shape grammars are a powerful and appealing formalism for automatic shape generation in computer-based design systems. This paper presents a proposal complementing the generative power of shape grammars with reinforcement learning techniques. We use simple (naive) shape grammars capable of generating a large variety of different designs. In order to generate those designs that comply with given design requirements, the grammar is subject to a process of machine learning using reinforcement learning techniques. Based on this method, we have developed a system for architectural design, aimed at generating two-dimensional layout schemes of single-family housing units. Using relatively simple grammar rules, we learn to generate schemes that satisfy a set of requirements stated in a design guideline. Obtained results are presented and discussed.     

Image Registration in a Coarse Three-Dimensional Virtual Environment

In recent years, the availability of off‐the‐shelf geometric data for an urban environment has increased. During rendering, ground level images are mapped onto the façades of the buildings to improve the visual quality of the scene. This paper focuses on a technique that enables ground level images to be automatically integrated into an existing coarse three‐dimensional environment. The approach utilises the planar nature of architectural scenes to enable the automatic extraction of a building façade from an image and its registration into the virtual environment.     

Lighting Layout Optimization for 3D Indoor Scenes

As the number of models for 3D indoor scenes are increasing rapidly, methods for generating the lighting layout have also become increasingly important. This paper presents a novel method that creates optimal placements and intensities of a set of lights in indoor scenes. Our method is characterized by designing the objective functions for the optimization based on the lighting guidelines used in the interior design field. Specifically, to apply major elements of the lighting guideline, we identify three criteria, namely the structure, function, and aesthetics, that are suitable for the virtual space and quantify them through a set of objective terms: pairwise relation, hierarchy, circulation, illuminance, and collision. Given an indoor scene with properly arranged furniture as input, our method combines the procedural and optimization‐based approaches to generate lighting layouts appropriate to the geometric and functional characteristics of the input scene. The effectiveness of our method is demonstrated with an ablation study of cost terms for the optimization and a user study for perceptual evaluation.     

Enhancing Urban Façades via LiDAR‐Based Sculpting

Buildings with symmetrical façades are ubiquitous in urban landscapes and detailed models of these buildings enhance the visual realism of digital urban scenes. However, a vast majority of the existing urban building models in web‐based 3D maps such as Google earth are either less detailed or heavily rely on texturing to render the details. We present a new framework for enhancing the details of such coarse models, using the geometry and symmetry inferred from the light detection and ranging (LiDAR) scans and 2D templates. The user‐defined 2D templates, referred to as coded planar meshes (CPMs), encodes the geometry of the smallest repeating 3D structures of the façades via face codes. Our encoding scheme, take into account the directions, type as well as the offset distance of the sculpting to be applied at the respective locations on the coarse model. In our approach, LiDAR scan is registered with the coarse models taken from Google earth 3D or Bing maps 3D and decomposed into dominant planar segments (each representing the frontal or lateral walls of the building). The façade segments are then split into horizontal and vertical tiles using a weighted point count function defined over the window or door boundaries. This is followed by an automatic identification of CPM locations with the help of a template fitting algorithm that respects the alignment regularity as well as the inter‐element spacing on the façade layout. Finally, 3D boolean sculpting operations are applied over the boxes induced by CPMs and the coarse model, and a detailed 3D model is generated. The proposed framework is capable of modelling details even with occluded scans and enhances not only the frontal façades (facing to the streets) but also the lateral façades of the buildings. We demonstrate the potentials of the proposed framework by providing several examples of enhanced Google earth models and highlight the advantages of our method when designing photo‐realistic urban façades.     

Parallel generation of architecture on the GPU

In this paper, we present a novel approach for the parallel evaluation of procedural shape grammars on the graphics processing unit (GPU). Unlike previous approaches that are either limited in the kind of shapes they allow, the amount of parallelism they can take advantage of, or both, our method supports state of the art procedural modeling including stochasticity and context‐sensitivity. To increase parallelism, we explicitly express independence in the grammar, reduce inter‐rule dependencies required for context‐sensitive evaluation, and introduce intra‐rule parallelism. Our rule scheduling scheme avoids unnecessary back and forth between CPU and GPU and reduces round trips to slow global memory by dynamically grouping rules in on‐chip shared memory. Our GPU shape grammar implementation is multiple orders of magnitude faster than the standard in CPU‐based rule evaluation, while offering equal expressive power. In comparison to the state of the art in GPU shape grammar derivation, our approach is nearly 50 times faster, while adding support for geometric context‐sensitivity.     

Real-time collaborative reconstruction of digital building models with mobile devices

Many software-based building processes require digital building models. Since the building stock does not have sufficient data in this regard, the demand for Scan-to-BIM processes is increasing. In this paper we present a system for the reconstruction of ‘as-built’ BIM content of house interiors based on the Google Tango technology. The strength of our approach is the use of low-cost mobile scanning devices and a client-server system that allows for a real-time collaborative scanning and reconstruction of indoor scenes. We developed a server application that continuously aggregates scan data of multiple scanning devices (clients) and applies the data stream to a real-time post-processing pipeline to reconstruct rooms, walls, doors and windows. The reconstruction result is then distributed to all clients, where it is visualized in real time. The collaborative workflow and real-time data processing make our system especially useful in situations that are time-critical and require concurrent collection and processing of data. One of our targeted use cases therefore is the model generation for crime scene documentation. The effectiveness of our approach was demonstrated on three test sites. Our results compare well to other state-of-art methods regarding the reconstruction of walls, but they also revealed potential for improvement regarding the detection of doors and windows in occluded and cluttered environments.     

Parsing Arabic using induced probabilistic context free grammar

The importance of the parsing task for NLP applications is well understood. However developing parsers remains difficult because of the complexity of the Arabic language. Most parsers are based on syntactic grammars that describe the syntactic structures of a language. The development of these grammars is laborious and time consuming. In this paper we present our method for building an Arabic parser based on an induced grammar, PCFG grammar. We first induce the PCFG grammar from an Arabic Treebank. Then, we implement the parser that assigns syntactic structure to each input sentence. The parser is tested on sentences extracted from the treebank (1650 sentences).We calculate the precision, recall and f-measure. Our experimental results showed the efficiency of the proposed parser for parsing modern standard Arabic sentences (Precision: 83.59 %, Recall: 82.98 % and F-measure: 83.23 %).