We present a method for automatically generating polygonal shapes from an example using a graph grammar. Most procedural modeling techniques use grammars with manually created rules, but our method can create them automatically from an example. Our graph grammars generate graphs that are locally similar to a given example. We disassemble the input into small pieces called primitives and then reassemble the primitives into new graphs. We organize all possible locally similar graphs into a hierarchy and find matching graphs within the hierarchy. These matches are used to create a graph grammar that can construct every locally similar graph. Our method generates graphs using the grammar and then converts them into a planar graph drawing to produce the final shape.

We present an interactive furniture layout system that assists users by suggesting furniture arrangements that are based on interior design guidelines. Our system incorporates the layout guidelines as terms in a density function and generates layout suggestions by rapidly sampling the density function using a hardware-accelerated Monte Carlo sampler. Our results demonstrate that the suggestion generation functionality measurably increases the quality of furniture arrangements produced by participants with no prior training in interior design.

We present a method for procedurally modeling general complex 3D shapes. Our approach can automatically generate complex models of buildings, man-made structures, or urban data sets in a few minutes based on user-defined inputs. The algorithm attempts to generate complex 3D models that resemble a user-defined input model and satisfy various dimensional, geometric, and algebraic constraints to control the shape. These constraints are used to capture the intent of the user and generate shapes that look more natural. We also describe efficient techniques to handle complex shapes and highlight its performance on many different types of models. We compare model synthesis algorithms with other procedural modeling techniques, discuss the advantages of different approaches, and describe as close connection between model synthesis and context-sensitive grammars.

Abstract We present a novel method for the synthesis and animation of realistic traffic flows on large‐scale road networks. Our technique is based on a continuum model of traffic flow we extend to correctly handle lane changes and merges, as well as traffic behaviors due to changes in speed limit. We demonstrate how our method can be applied to the animation of many vehicles in a large‐scale traffic network at interactive rates and show that our method can simulate believable traffic flows on publicly‐available, real‐world road data. We furthermore demonstrate the scalability of this technique on many‐core systems.

We present a method for automated generation of building layouts for computer graphics applications. Our approach is motivated by the layout design process developed in architecture. Given a set of high-level requirements, an architectural program is synthesized using a Bayesian network trained on real-world data. The architectural program is realized in a set of floor plans, obtained through stochastic optimization. The floor plans are used to construct a complete three-dimensional building with internal structure. We demonstrate a variety of computer-generated buildings produced by the presented approach.

We present a method for procedurally modeling general complex 3D shapes. Our approach is targeted towards applications in digital entertainment and gaming and can automatically generate complex models of buildings, man-made structures, or urban datasets in a few minutes based on user-defined inputs. The algorithm attempts to generate results that resemble a user-defined input model and that satisfy various dimensional, geometric, and algebraic constraints. These constraints are used to capture the intent of the user and generate shapes that look more natural. We also describe efficient techniques to handle complex shapes and demonstrate their performance on many different types of models.

We present a novel method for procedurally modeling large complex shapes. Our approach is general-purpose and takes as input any 3D polyhedral model provided by a user. The algorithm exploits the connectivity between the adjacent boundary features of the input model and computes an output model that has similar connected features and resembles the input. No additional user input is needed to guide the model generation and the algorithm proceeds automatically. In practice, our algorithm is simple to implement and can generate a variety of complex shapes representing buildings, landscapes, and 3D fractal shapes in a few minutes.

We present a viewpoint-based approach for the quick fusion of multiple stereo depth maps. Our method selects depth estimates for each pixel that minimize violations of visibility constraints and thus remove errors and inconsistencies from the depth maps to produce a consistent surface. We advocate a two-stage process in which the first stage generates potentially noisy, overlapping depth maps from a set of calibrated images and the second stage fuses these depth maps to obtain an integrated surface with higher accuracy, suppressed noise, and reduced redundancy. We show that by dividing the processing into two stages we are able to achieve a very high throughput because we are able to use a computationally cheap stereo algorithm and because this architecture is amenable to hardware-accelerated (GPU) implementations. A rigorous formulation based on the notion of stability of a depth estimate is presented first. It aims to determine the validity of a depth estimate by rendering multiple depth maps into the reference view as well as rendering the reference depth map into the other views in order to detect occlusions and free- space violations. We also present an approximate alternative formulation that selects and validates only one hypothesis based on confidence. Both formulations enable us to perform video-based reconstruction at up to 25 frames per second. We show results on the multi-view stereo evaluation benchmark datasets and several outdoors video sequences. Extensive quantitative analysis is performed using an accurately surveyed model of a real building as ground truth.

We present an evaluation methodology and data for large scale video-based 3D reconstruction. We evaluate the effects of several parameters and draw conclusions that can be useful for practical systems operating in uncontrolled environments. Unlike the benchmark datasets used for the binocular stereo and multi-view reconstruction evaluations, which were collected under well-controlled conditions, our datasets are captured outdoors using video cameras mounted on a moving vehicle. As a result, the videos are much more realistic and include phenomena such as exposure changes from viewing both bright and dim surfaces, objects at varying distances from the camera, and objects of varying size and degrees of texture. The dataset includes ground truth models and precise camera pose information. We also present an evaluation methodology applicable to reconstructions of large scale environments. We evaluate the accuracy and completeness of reconstructions obtained by two fast, visibility-based depth map fusion algorithms as parameters vary.

Model synthesis is a new approach to 3D modeling which automatically generates large models that resemble a small example model provided by the user. Model synthesis extends the 2D texture synthesis problem into higher dimensions and can be used to model many different objects and environments. The user only needs to provide an appropriate example model and does not need to provide any other instructions about how to generate the model. Model synthesis can be used to create symmetric models, models that change over time, and models that fit soft constraints. There are two important differences between our method and existing texture synthesis algorithms. The first is the use of a global search to find potential conflicts before adding new material to the model. The second difference is that we divide the problem of generating a large model into smaller subproblems which are easier to solve.