Gaze interaction presents a promising avenue in Virtual Reality (VR) due to its intuitive and efficient user experience. Yet, the depth control inherent in our visual system remains underutilized in current methods. In this study, we introduce FocusFlow, a hands-free interaction method that capitalizes on human visual depth perception within the 3D scenes of Virtual Reality. We first develop a binocular visual depth detection algorithm to understand eye input characteristics. We then propose a layer-based user interface and introduce the concept of “Virtual Window” that offers an intuitive and robust gaze-depth VR interaction, despite the constraints of visual depth accuracy and precision spatially at further distances. Finally, to help novice users actively manipulate their visual depth, we propose two learning strategies that use different visual cues to help users master visual depth control. Our user studies on 24 participants demonstrate the usability of our proposed virtual window concept as a gaze-depth interaction method. In addition, our findings reveal that the user experience can be enhanced through an effective learning process with adaptive visual cues, helping users to develop muscle memory for this brand-new input mechanism. We conclude the paper by discussing potential future research topics of gaze-depth interaction.

We are excited to introduce CoMoTo -- the Collaboration Modeling Toolkit -- a new, web-based application that expands and enhances well-known software similarity detection systems. CoMoTo is an end-to-end data management, analysis, and visualization system whose purpose is to assist instructors of courses requiring programming exercises to monitor and investigate the extent of student collaboration, both allowed and illicit. We describe CoMoTo's interface, which was designed to facilitate scrutiny of collaboration data projected along student, course, assignment, etc. attributes, and to allow for interactive visualization of pairwise similarity measures via a dynamic graph. We also elaborate on the details of CoMoTo's implementation. Finally, we briefly discuss two use cases that foreshadow CoMoTo's broad utility in student code analysis, not only for plagiarism detection, but also for investigating early student coding styles, and for evaluating software similarity detection systems, themselves.

Improving the quality of tetrahedral meshes is an important operation in many scientific computing applications. Meshes with badly shaped elements impact both the accuracy and convergence of scientific applications. State-of-the-art mesh improvement techniques rely on sophisticated numerical optimization methods such as feasible Newton or conjugate gradient. Unfortunately, these methods cannot be practically applied to very large meshes due to their global nature. Our contribution in this paper is to describe a streaming framework for tetrahedral mesh optimization. This framework enables the optimization of meshes an order of magnitude larger than previously feasible, effectively optimizing meshes too large to fit in memory. Our results show that streaming is typically faster than global optimization and results in comparable mesh quality. This leads us to conclude that streaming extends mesh optimization to a new class of mesh sizes without compromising the quality of the optimized mesh.

We present a new external memory multiresolution surface representation for massive polygonal meshes. Previous methods for building such data structures have relied on resampled surface data or employed memory intensive construction algorithms that do not scale well. Our proposed representation combines efficient access to sampled surface data with access to the original surface. The construction algorithm for the surface representation exhibits memory requirements that are insensitive to the size of the input mesh, allowing it to process meshes containing hundreds of millions of polygons. The multiresolution nature of the surface representation has allowed us to develop efficient algorithms for view-dependent rendering, approximate collision detection, and adaptive simplification of massive meshes. The empirical performance of these algorithms demonstrates that the underlying data structure is a powerful and flexible tool for operating on massive geometric data.

We present a new multiphase method for efficiently simplifying polygonal surface models of arbitrary size. It operates by combining an initial out-of-core uniform clustering phase with a subsequent in-core iterative edge contraction phase. These two phases are both driven by quadric error metrics, and quadrics are used to pass information about the original surface between phases. The result is a method that produces approximations of a quality comparable to quadric-based iterative edge contraction, but at a fraction of the cost in terms of running time and memory consumption.

The growing availability of massive polygonal models, and the inability of most existing visualization tools to work with such data, has created a pressing need for memory-efficient methods capable of simplifying very large meshes. In this paper, we present a method for performing adaptive simplification of polygonal meshes that are too large to fit in-core. Our algorithm performs two passes over an input mesh. In the first pass, the model is quantized using a uniform grid, and surface information is accumulated in the form of quadrics and dual quadrics. This sampling is then used to construct a BSP-tree in which the partitioning planes are determined by the dual quadrics. In the final pass, the original vertices are clustered using the BSP-tree, yielding an adaptive approximation of the original mesh. The BSP-tree describes a natural simplification hierarchy, making it possible to generate a progressive transmission and construct level-of-detail representations. In this way, the algorithm provides some of the features associated with more expensive edge contraction methods while maintaining greater computational efficiency. In addition to performing adaptive simplification, our algorithm exhibits output-sensitive memory requirements and allows fine control over the size of the simplified mesh.

Complex, distributed applications pose new challenges for performance analysis and optimization. This paper outlines an online approach to performance analysis where developers are active participants, using integrated measurement and immersive performance visualization to tune parallel and distributed applications.