Current sketch modeling methods face challenges in terms of efficiency and quality due to frequent view changes. To empower users to create models seamlessly and accurately from a single reference drawing, we present WidgetSketcher, a single-view, sketch-based modeling method that incorporates widget manipulations. To ensure seamlessness, we implement a single-view workflow in sketch-based modeling with designed widget manipulations and other interactions. Each user stroke is equipped with a transient axis widget, allowing 3D pose specification and part creation from a single view. To improve accuracy, we develop a two-stage shape generation algorithm, ensuring the precise generation of shapes with detailed features and asymmetry. Our method enables the quick and accurate creation of 3D models consistent with reference drawings, while eliminating frequent view changes. To demonstrate the efficiency of our method, we evaluate the system usability by conducting a user study and compare the results to state-of-the-art techniques.

Dynamic collaboration is a common form of human interaction in everyday life. With the increasing support of virtual reality (VR) applications for such collaborative joint actions, understanding how visualization design choices affect collaboration becomes crucial. However, the impact of two key elements - avatar visibility and perspective - remains underexplored in dynamic collaborative tasks. To address this, we conducted two user studies representing distinct collaborative contexts: a proximal task and a spatially distributed task. Within these contexts, we investigated how varying levels of avatar visibility (full-body, upper-body, and head-and-hands) and perspectives (first-person perspective, 1PP, and third-person perspective, 3PP) influenced social presence and collaborative performance, including both task completion and team coordination aspects. Our findings revealed that 3PP significantly enhanced both social presence and team coordination compared to 1PP. The impact of avatar visibility was context-dependent: in environments with low dynamic complexity, even low-visibility avatars maintained effective collaboration, while complex spatial tasks benefited from increased avatar visibility. Furthermore, our research demonstrated that 3PP mitigated the negative effects of low-visibility avatars in 1PP, lessening coordination conflicts in spatially complex scenarios. Based on these findings, we propose design recommendations for avatar visibility and perspective choices in dynamic collaborative environments. Our research advances the understanding of how these fundamental visual design elements shape VR collaboration, informing the design of future interactive environments.

Robust 3D perception using LiDAR is of prime importance for robotics, and its fundamental core lies in precise object modelling resisting to noise and outliers. In this paper, a precise 3D object modelling algorithm is designed especially for the intelligent vehicles. The proposed algorithm is advantageous by leveraging the crucial traffic geometrical prior of road surface profile, and both the noise and outliers are elegantly handled by robust correntropy-based metric. More specifically, the road surface correction (RSC) method transforms each individual LiDAR measurement from its locally planar road surface to a globally ideal plane. This procedure essentially guarantees the reduction of vehicle's motion from arbitrary 3D motion to physically feasible 2D motion. To deal with the noise and outliers, a correntropy-based multi-frame matching (CorrMM) algorithm is proposed which has a robust objective function with respect to point-to-plane residual error. An efficient solver inspired by M-estimator and retraction technique on Lie group is developed, which elegantly converts the optimization of highly non-linear objective function into a simple quadratic programming (QP) problem. Extensive experimental results validate that the proposed algorithm attains more crisper 3D object models than several state-of-the-art algorithms on a challenging real traffic dataset.

Research in the areas related to driving behavior, e.g., behavior modeling and prediction, requires datasets with highly interactive vehicle motions. Existing public vehicle motion datasets emphasize increasing the number of vehicles and time duration, but behavior-related researchers are suffering from two factors. First, strong interactions among vehicles are not well addressed and datasets are of relatively low-density to observe meaningful interactions. Second, most of the existing datasets are missing the map information with reference paths which is essential for driving-behavior-related research. To address this issue, a dataset with highly interactive vehicle motions is constructed in this paper. A variety of challenging driving scenarios such as unsignalized intersections and roundabouts are included. Reference paths are also constructed from motion data along with high-definition maps so that key features can be generated for both prediction and planning algorithms. Moreover, we propose a set of metrics to extract the interactive motions in different maps, including the minimum difference of time to collision point (MDTTC) and duration of waiting period. Such metrics are used to quantity the interaction density of the dataset. We also give several representative results on prediction and motion generation utilizing the constructed dataset to demonstrate how the dataset can facilitate research in the area of driving behavior.

EDGE (Explicit Data Graph Execution) architectures, a class of architectures distinct from traditional RISC and CISC architectures, have advantages that align well with current technology trends such as power limitations and the need for adaptive exploitation of parallelism. To better understand the architectural and microarchitectural design spaces of EDGE architectures, we have developed a flexible M5-based simulator for EDGE architectures. m5_edge includes a general high-level timing model and ISA support of one specific EDGE ISA. The high-level timing model is not designed to target a specific implementation but common characteristics of EDGE architectures, permitting faster development of a range of microarchitectures. The M5 infrastructure was used because of its high functionality and performance fidelity. The specific EDGE ISA we support is the TRIPS ISA, due to its well-specified ISA and relatively mature compiler. m5_edge can execute binaries generated by the TRIPS toolchain and provides a high-level simulation template for EDGE architectures. Our experimental results show that the difference in execution cycles of m5_edge is within 11% on average, compared to the cycle-accurate simulator provided by the TRIPS group. Thus, m5_edge benefits from both the flexible infrastructure of M5 and acceptable model accuracy, while maintaining both reasonable simulation speed and the ability to quickly explore EDGE-based microarchitectural design spaces.