We propose a framework for global registration of building scans. The first contribution of our work is to detect and use portals (e.g., doors and windows) to improve the local registration between two scans. Our second contribution is an optimization based on a linear integer programming formulation. We abstract each scan as a block and model the blocks registration as an optimization problem that aims at maximizing the overall matching score of the entire scene. We propose an efficient solution to this optimization problem by iteratively detecting and adding local constraints. We demonstrate the effectiveness of the proposed method on buildings of various styles and that our approach is superior to the current state of the art.

We study the design and optimization of statically sound and materially efficient space structures constructed by connected beams. We propose a systematic computational framework for the design of space structures that incorporates static soundness, approximation of reference surfaces, boundary alignment, and geometric regularity. To tackle this challenging problem, we first jointly optimize node positions and connectivity through a nonlinear continuous optimization algorithm. Next, with fixed nodes and connectivity, we formulate the assignment of beam cross sections as a mixed-integer programming problem with a bilinear objective function and quadratic constraints. We solve this problem with a novel and practical alternating direction method based on linear programming relaxation. The capability and efficiency of the algorithms and the computational framework are validated by a variety of examples and comparisons.

Conventional pipelines for capturing, displaying, and storing images are usually defined as a series of cascaded modules, each responsible for addressing a particular problem. While this divide-and-conquer approach offers many benefits, it also introduces a cumulative error, as each step in the pipeline only considers the output of the previous step, not the original sensor data. We propose an end-to-end system that is aware of the camera and image model, enforces natural-image priors, while jointly accounting for common image processing steps like demosaicking, denoising, deconvolution, and so forth, all directly in a given output representation (e.g., YUV, DCT). Our system is flexible and we demonstrate it on regular Bayer images as well as images from custom sensors. In all cases, we achieve large improvements in image quality and signal reconstruction compared to state-of-the-art techniques. Finally, we show that our approach is capable of very efficiently handling high-resolution images, making even mobile implementations feasible.

We analyze light propagation in an unknown scene using projectors and cameras that operate at transient timescales. In this new photography regime, the projector emits a spatio-temporal 3D signal and the camera receives a transformed version of it, determined by the set of all light transport paths through the scene and the time delays they induce. The underlying 3D-to-3D transformation encodes scene geometry and global transport in great detail, but individual transport components (e.g., direct reflections, inter-reflections, caustics, etc.) are coupled nontrivially in both space and time. To overcome this complexity, we observe that transient light transport is always separable in the temporal frequency domain. This makes it possible to analyze transient transport one temporal frequency at a time by trivially adapting techniques from conventional projector-to-camera transport. We use this idea in a prototype that offers three never-seen-before abilities: (1) acquiring time-of-flight depth images that are robust to general indirect transport, such as interreflections and caustics; (2) distinguishing between direct views of objects and their mirror reflection; and (3) using a photonic mixer device to capture sharp, evolving wavefronts of "light-in-flight".

We propose a new framework to model the exterior of residential buildings. The main goal of our work is to design a model that can be learned from data that is observable from the outside of a building and that can be trained with widely available data such as aerial images and street-view images. First, we propose a parametric model to describe the exterior of a building (with a varying number of parameters) and propose a set of attributes as a building representation with fixed dimensionality. Second, we propose a hierarchical graphical model with hidden variables to encode the relationships between building attributes and learn both the structure and parameters of the model from the database. Third, we propose optimization algorithms to generate three-dimensional models based on building attributes sampled from the graphical model. Finally, we demonstrate our framework by synthesizing new building models and completing partially observed building models from photographs.

We propose a method for propagating edit operations in 2D vector graphics, based on geometric relationship functions. These functions quantify the geometric relationship of a point to a polygon, such as the distance to the boundary or the direction to the closest corner vertex. The level sets of the relationship functions describe points with the same relationship to a polygon. For a given query point, we first determine a set of relationships to local features, construct all level sets for these relationships, and accumulate them. The maxima of the resulting distribution are points with similar geometric relationships. We show extensions to handle mirror symmetries, and discuss the use of relationship functions as local coordinate systems. Our method can be applied, for example, to interactive floorplan editing, and it is especially useful for large layouts, where individual edits would be cumbersome. We demonstrate populating 2D layouts with tens to hundreds of objects by propagating relatively few edit operations. © 2014 ACM 0730-0301/2014/03- ART15 $15.00.

In this paper, we address the following research problem: How can we generate a meaningful split grammar that explains a given facade layout? To evaluate if a grammar is meaningful, we propose a cost function based on the description length and minimize this cost using an approximate dynamic programming framework. Our evaluation indicates that our framework extracts meaningful split grammars that are competitive with those of expert users, while some users and all competing automatic solutions are less successful. Copyright © ACM.

In this paper, we tackle the problem of tiling a domain with a set of deformable templates. A valid solution to this problem completely covers the domain with templates such that the templates do not overlap. We generalize existing specialized solutions and formulate a general layout problem by modeling important constraints and admissible template deformations. Our main idea is to break the layout algorithm into two steps: a discrete step to lay out the approximate template positions and a continuous step to refine the template shapes. Our approach is suitable for a large class of applications, including floorplans, urban layouts, and arts and design. Copyright © ACM.

(Figure Presented) We present a method to complete missing structures in facade layouts. Starting from an abstraction of the partially observed layout as a set of shapes, we can propose one or multiple possible completed layouts. Structure completion with large missing parts is an ill-posed problem. Therefore, we combine two sources of information to derive our solution: the observed shapes and a database of complete layouts. The problem is also very difficult, because shape positions and attributes have to be estimated jointly. Our proposed solution is to break the problem into two components: a statistical model to evaluate layouts and a planning algorithm to generate candidate layouts. This ensures that the completed result is consistent with the observation and the layouts in the database.

PushPull tools are implemented in most commercial 3D modeling suites. Their purpose is to intuitively transform a face, edge, or vertex, and then to adapt the polygonal mesh locally. However, previous approaches have limitations: Some allow adjustments only when adjacent faces are orthogonal; others support slanted surfaces but never create new details. Moreover, self-intersections and edge-collapses during editing are either ignored or work only partially for solid geometry. To overcome these limitations, we introduce the PushPull++ tool for rapid polygonal modeling. In our solution, we contribute novel methods for adaptive face insertion, adjacent face updates, edge collapse handling, and an intuitive user interface that automatically proposes useful drag directions. We show that PushPull++ reduces the complexity of common modeling tasks by up to an order of magnitude when compared with existing tools. Copyright © ACM.