Paper Summary

Acoustic filters are used to “produce a desired sound pitch or to attenuate undesired noise”, and find many applications in the real world, from wind instruments (e.g., flutes, trumpets, clarinets, etc.) and mufflers (e.g., engine noise muffler, acoustic earmuffs) to hearing aids. However, designing customized acoustic filters with specific properties remains a challenge, with the current approach(s) being “complicated and unintuitive” due to their iterative trail-and-error based process. While the design space for these acoustic filters has traditionally been limited to simple geometric shapes, the recent strides in additive manufacturing have enabled the fabrication of complex geometries, thus opening up new possibilities for the expanding the design space. Motivated by this, this paper presents Acoustic Voxels, a computational method for the design of acoustic filters, which relies on modular design based on a simple shape primitive (a hollow cube with circular holes on some or all of its faces) to build a complex assembly. Moreover, the modular design enables a “fast and accurate” estimation of a given assembly’s acoustic performance, which in turn allows for optimization of its structure to achieve desired filtering properties. While the paper provides detailed mathematical formulations of the proposed method, in the interest of brevity, the key details are presented in this summary. ...

The design of furniture consists of two major components: geometric modeling and physical validity of shapes. While advances in software for 3D modeling have made it easier for even inexperienced users to design shapes and thus make content creation easy, there is a disconnect between the geometric design and the physical functionality assessment. The typical workflow for a designer is to create a 3D model followed by validation using a physical simulator, and depending on the validation results, the designer re-iterates the whole process. This is tantamount to trial-and-error, making it a slow process, not to mention (a) the lack of any feedback to the designer specific to why the design failed and (b) the inherent limited exploration of novel shapes by encouraging designers to opt for standard geometric shapes only. While there have been previous work on suggestive modeling and interactive shape exploration, these works are limited in their feedback such as providing only a binary response about whether the design is valid or not, lack of informed exploration, etc. This paper proposes “a computational design framework for efficient and intuitive exploration” of physically valid furniture design shapes. In particular, the framework allows constrained modeling of nail-jointed furniture design of furniture using medium density fiberboard, and is constrained on 3 conditions: connectivity, durability, and stability. The paper describes the proposed method and the theoretical justifications in explicit detail, and we provide a brief summary of the key details here. ...

Compound graphs, a frequently encountered type of data set, have a hierarchical tree structure with parent-child relations (‘inclusion’ relations) and non-hierarchical relations between leaf nodes (‘adjacency’ relations). Such datasets are common in software systems, social networks, and citation networks, amongst other scenarios. Visualizing these data sets is difficult because the addition of adjacency relations in any existing tree visualization method results in visual clutter. Moreover, using a generic visualization approach for these yields poor results too because of the difficulty of separating the inclusion and the adjacency relations. The existing methods for visualizing compound graphs and compound directed graphs (hereafter collectively referred to as compound (di)graphs) have numerous shortcomings, such as the inefficient usage of the available space (radial and balloon layout-based tree visualization techniques), the lack of flexibility (methods for drawing clustered graphs), inability to scale well for compound (di)graphs with large hierarchies (ArcTree-based visualization), unintuitive presentation (matrix view based methods), and excessive clutter because of several “extra routing nodes” introduced by binary splits (flow map layouts). Drawing inspiration from the management and routing of electrical and network cables, this paper presents hierarchical edge bundles for visualizing compound (di)graphs. It is a flexible and an intuitive technique that can be used in conjunction with existing tree visualization methods and reduces visual clutter when working with a high count of adjacency relations. ...

Over the last couple of decades, large multi-dimensional databases have become ubiquitous in a vast array of application areas, such as corporate data warehouses as well as projects in scientific computing such as the Human Genome Project and the Digital Sky Survey. One of the major challenges in extracting meaningful information from such large scale databases is the “discover structure, find patterns, and derive causal relationships” from the data. A popular approach is to treat these databases as $n$-dimensional cubes, where each dimension corresponds to a dimension in the relational schema. One of the most popular interfaces for working with multi-dimensional databases is Pivot Table, largely popularized by Microsoft Excel, which allows the aforementioned data cubes to be rotated or pivoted so as to encode its various dimensions as rows or columns of the table. Previous work in this area can broadly be categorized into 3 main areas of focus: (a) formalisms for graphical specifications which include earlier works such as Bertin’s ‘Semiology of Graphics’ as well as recent work such as Wilkinson’s ‘The Grammar of Graphics’, (b) table-based displays which include static table displays such as scatterplot matrices and Tellis displays as well as interactive ones such as Pivot Tables, and (c) tools for visual exploration of datasets, such as VQE, Visage, DEVise, Tioga-2, and VisDB. This paper presents Polaris, a multi-dimensional database exploration interface extending the Pivot Table interface and allowing for direct generation of “rich, expressive set of” graphical displays. Using an algebraic formalism over the database fields, Polaris constructs tables consisting of layers and panes, with the possibility of a different graphics in each pane. For the sake of brevity of this summary, although the paper provides detailed description of the Polaris system, we only discuss its major components here. ...

Given the importance and the ubiquity of indoor spaces in our everyday lives, the ability to have computer models which can understand, model, and synthesize indoor scenes is of vital importance for many industries such as but not limited to interior design, architecture, gaming, virtual reality, etc. Previous works towards this goal have relied on constrained synthesis of scenes with statistical priors on object pair relationships, “human-centric relationship priors”, or constraints based on “hand-crafted interior design principles”. Moreover, owing to the difficulty of unconstrained room-scale synthesis of indoor scenes, prior work has focused on either small regions within a room or additional inputs (in the form of fixed set of objects, manually specified relationships, natural language description, sketch, or 3D scan of the room) as constraints, and deep generative models such as GANs and VAEs struggle with producing multi-modal outputs. Driven by the success of convolutional neural networks (CNNs) in scene synthesis tasks and the availability of large 3D scene datasets, this paper proposes the first CNN-based autoregressive model to design interior spaces, where given the wall structure and the type of a room, the model predicts the selection and placement of objects. ...