Abstract We introduce a method to design lightweight shell objects that are structurally robust under the external forces they may experience during use. Given an input 3D model and a general description of the external forces, our algorithm generates a structurally‐sound minimum weight shell object. Our approach works by altering the local shell thickness repeatedly based on the stresses that develop inside the object. A key issue in shell design is that large thickness values might result in self‐intersections on the inner boundary creating a significant computational challenge during optimization. To address this, we propose a shape parametrization based on the solution to the Laplace's equation that guarantees smooth and intersection‐free shell boundaries. Combined with our gradient‐free optimization algorithm, our method provides a practical solution to the structural design of hollow objects with a single inner cavity. We demonstrate our method on a variety of problems with arbitrary 3D models under complex force configurations and validate its performance with physical experiments.

We present 4DMesh, a method of combining shrinking and bending thermoplastic actuators with customized geometric algorithms to 4D print and morph centimeter- to meter-sized functional non-developable surfaces. We will share two end-to-end inverse design algorithms. With our tools, users can input CAD models of target surfaces and produce respective printable files. The flat sheet printed can morph into target surfaces when triggered by heat. This system saves shipping and packaging costs, in addition to enabling customizability for the design of relatively large non-developable structures. We designed a few functional artifacts to leverage the advantage of non-developable surfaces for their unique functionalities in aesthetics, mechanical strength, geometric ergonomics and other functionalities. In addition, we demonstrated how this technique can potentially be adapted to customize molds for industrial parts (e.g., car, boat, etc.) in the future.

We introduce a lightweight structure optimization approach for problems in which there is uncertainty in the force locations. Such uncertainty may arise due to force contact locations that change during use or are simply unknown a priori. Given an input 3D model, regions on its boundary where arbitrary normal forces may make contact, and a total force-magnitude budget, our algorithm generates a minimum weight 3D structure that withstands any force configuration capped by the budget. Our approach works by repeatedly finding the most critical force configuration and altering the internal structure accordingly. A key issue, however, is that the critical force configuration changes as the structure evolves, resulting in a significant computational challenge. To address this, we propose an efficient critical instant analysis approach. Combined with a reduced order formulation, our method provides a practical solution to the structural optimization problem. We demonstrate our method on a variety of models and validate it with mechanical tests.

Procedural modeling systems allow users to create high quality content through parametric, conditional or stochastic rule sets. While such approaches create an abstraction layer by freeing the user from direct geometry editing, the nonlinear nature and the high number of parameters associated with such design spaces result in arduous modeling experiences for non-expert users. We propose a method to enable intuitive exploration of such high dimensional procedural modeling spaces within a lower dimensional space learned through autoencoder network training. Our method automatically generates a representative training dataset from the procedural modeling rule set based on shape similarity features. We then leverage the samples in this dataset to train an autoencoder neural network, while also structuring the learned lower dimensional space for continuous exploration with respect to shape features. We demonstrate the efficacy our method with user studies where designers create content with more than 10-fold faster speeds using our system compared to the classic procedural modeling interface.

We propose a shape editing method where the user creates geometric deformations using a set of semantic attributes, thus avoiding the need for detailed geometric manipulations. In contrast to prior work, we focus on continuous deformations instead of discrete part substitutions. Our method provides a platform for quick design explorations and allows non-experts to produce semantically guided shape variations that are otherwise difficult to attain. We crowdsource a large set of pairwise comparisons between the semantic attributes and geometry and use this data to learn a continuous mapping from the semantic attributes to geometry. The resulting map enables simple and intuitive shape manipulations based solely on the learned attributes. We demonstrate our method on large datasets using two different user interaction modes and evaluate its usability with a set of user studies.

The ability to solve complex problems in which considerable latitude in solution is allowed is an important goal in engineering education. While feedback is critical to learning, traditional grading of handwritten homework rarely provides effective, timely feedback on attempts to solve complex problems. To address the research question of whether automated feedback can be provided on complex problem solving, a cognitive tutor approach is pursued, with truss problems in statics used as a test case. The tutor maintains a cognitive model of problem solving of the class of problems, and associates various solution steps with distinct skills or knowledge components. One can determine whether students learn individual skills by measuring the error rate as a function of practice. Data was obtained from students using the tutor to solve truss problems in place of the weekly homework assignment. Statistical analyses of the data showed that distinct skills involved very different error rates, justifying tracking them separately. Furthermore, for most skills, the error rate either was low from the start or decreased with practice, providing evidence that feedback improved performance.

In this paper, a method is presented that automatically recognizes kinematic mechanisms from textbook images using an evolutionary algorithm to complement computer vision techniques for object detection. Specifically, a nondominated sorting genetic algorithm (NSGA-II) is used to optimize the number and position of mechanical joints in an image and corresponding joint connections (i.e. rigid bodies) such that Pareto front solutions maximize image consistency and mechanical feasibility. A well-known object detector is used as an example method for locating joints, and local image features between pairwise detected joints are used to predict likely connections. The performance of the algorithm using these specific vision techniques is compared to a parameterized detection scheme in order to decouple the efficacy of the object detector from the evolutionary algorithm. Experiments were performed to validate this approach on selected images from a custom dataset, and the results demonstrate reasonable success in both accuracy and speed.

We present a method for learning custom deformation handles for an object, from a co-analysis of similar objects. Our approach identifies the geometric and spatial constraints among the different parts of an object, and makes this information available through abstract shape handles. These handles allow the user to prescribe arbitrary deformation directives including free-form surface deformations. However, only a subset of admissible deformations is enabled to the user as learned from the constraint space. Example applications are presented in shape editing, co-deformation and style transfer.

We present a co-abstraction method that takes as input a collection of 3D objects, and produces a mutually consistent and individually identity-preserving abstraction of each object. In general, an abstraction is a simpler version of a shape that preserves its main characteristics. We hypothesize, however, that there is no single abstraction of an object. Instead, there is a variety of possible abstractions, and an admissible one can only be chosen conjointly with other objects' abstractions. To this end, we introduce a new approach that hierarchically generates a spectrum of abstractions for each model in a shape collection. Given the spectra, we compute the appropriate abstraction level for each model such that shape simplification and inter-set consistency are collectively maximized, while individual shape identities are preserved.

We propose a new sketch parsing and beautification method that converts digitally created design sketches into beautified line drawings. Our system uses a trainable, sequential bottom-up and top-down stroke clustering method that learns how to parse input pen strokes into groups of strokes each representing a single curve, followed by point-cloud ordering that facilitates curve fitting and smoothing. This approach enables greater conceptual freedom during visual ideation activities by allowing designers to develop their sketches using multiple, casually drawn strokes without requiring them to indicate the separation between different stroke groups. With the proposed method, raw sketches are seamlessly converted into vectorized geometric models, thus, facilitating downstream assessment and editing activities.

This paper describes a new approach to industrial styling design that combines the advantages of pen-based sketching with concepts from variational design to facilitate rapid and fluid development of 3D geometry. The approach is particularly useful for designing products that are primarily stylistic variations of existing ones. The input to the system is a 2D concept sketch of the object, and a generic 3D wireframe template. In the first step, the underlying template is aligned with the input sketch using a camera calibration algorithm. Next, the user traces the feature edges of the sketch on the computer screen; user's 2D strokes are processed and interpreted in 3D to modify the edges of the template. The resulting wireframe is then surfaced, followed by a user-controlled refinement of the initial surfaces using physically-based deformation techniques. Finally, new design edges can be added and manipulated through direct sketching over existing surfaces. Our preliminary evaluation involving several industrial products have demonstrated that with the proposed system, design times can be significantly reduced compared to those obtained through conventional software.

Sim-U-Sketch is an experimental sketch-based interface we developed for Matlab®'s Simulink® software package. With this tool, users can construct functional Simulink models simply by drawing sketches on a computer screen. To support iterative design, Sim-U-Sketch allows users to interact with their sketches in real time to modify existing objects and add new ones. The system is equipped with a domain-independent, trainable symbol recognizer that can learn new symbols from single prototype examples. This makes our system easily extensible and customizable to new domains and unique drawing styles.

A long standing challenge in pen-based computer interaction is the ability to make sense of informal sketches. A main difficulty lies in reliably extracting and recognizing the intended set of visual objects from a continuous stream of pen strokes. Existing pen-based systems either avoid these issues altogether, thus resulting in the equivalent of a drawing program, or rely on algorithms that place unnatural constraints on the way the user draws. As one step toward alleviating these difficulties, we present an integrated sketch parsing and recognition approach designed to enable natural, fluid, sketch-based computer interaction. The techniques presented in this paper are oriented toward the domain of network diagrams. In the first step of our approach, the stream of pen strokes is examined to identify the arrows in the sketch. The identified arrows then anchor a spatial analysis which groups the uninterpreted strokes into distinct clusters, each representing a single object. Finally, a trainable shape recognizer, which is informed by the spatial analysis, is used to find the best interpretations of the clusters. Based on these concepts, we have built SimuSketch, a sketch-based interface for Matlab's Simulink software package. An evaluation of SimuSketch has indicated that even novice users can effectively utilize our system to solve real engineering problems without having to know much about the underlying recognition techniques.