Line-based density plots are used to reduce visual clutter in line charts with a multitude of individual lines. However, these traditional density plots are often perceived ambiguously, which obstructs the user's identification of underlying trends in complex datasets. Thus, we propose a novel image space coloring method for line-based density plots that enhances their interpretability. Our method employs color not only to visually communicate data density but also to highlight similar regions in the plot, allowing users to identify and distinguish trends easily. We achieve this by performing hierarchical clustering based on the lines passing through each region and mapping the identified clusters to the hue circle using circular MDS. Additionally, we propose a heuristic approach to assign each line to the most probable cluster, enabling users to analyze density and individual lines. We motivate our method by conducting a small-scale user study, demonstrating the effectiveness of our method using synthetic and real-world datasets, and providing an interactive online tool for generating colored line-based density plots.

Image-guided drawing can compensate for a lack of skill but often requires a significant number of repetitive strokes to create textures. Existing automatic stroke synthesis methods are usually limited to predefined styles or require indirect manipulation that may break the spontaneous flow of drawing. We present an assisted drawing system to autocomplete repetitive short strokes during a user’s normal drawing process. Users draw over a reference image as usual; at the same time, our system silently analyzes the input strokes and the reference to infer strokes that follow the user’s input style when certain repetition is detected. Users can accept, modify, or ignore the system’s predictions and continue drawing, thus maintaining fluid control over drawing. Our key idea is to jointly analyze image regions and user input history to detect and predict repetition. The proposed system can effectively reduce the user’s workload when drawing repetitive short strokes, helping users to create results with rich patterns.

The recent advance in motion tracking (e.g., Visual Inertial Odometry) allows the use of a mobile phone as a 3D pen, thus significantly benefiting various mobile Augmented Reality (AR) applications based on 3D curve creation. However, when creating 3D curves on and around physical objects with mobile AR, tracking might be less robust or even lost due to camera occlusion or textureless scenes. This motivates us to study how to achieve natural interaction with minimum tracking errors during close interaction between a mobile phone and physical objects. To this end, we contribute an elicitation study on input point and phone grip, and a quantitative study on tracking errors. Based on the results, we present a system for direct 3D drawing with an AR-enabled mobile phone as a 3D pen, and interactive correction of 3D curves with tracking errors in mobile AR. We demonstrate the usefulness and effectiveness of our system for two applications: in-situ 3D drawing, and direct 3D measurement.

Abstract In recent years guider‐follower approaches show a promising solution to the challenging problem of last‐mile or indoor pedestrian navigation without micro‐maps or indoor floor plans for path planning. However, the success of such guider‐follower approaches is highly dependent on a set of manually and carefully chosen image or video checkpoints. This selection process is tedious and error‐prone. To address this issue, we first conduct a pilot study to understand how users as guiders select critical checkpoints from a video recorded while walking along a route, leading to a set of criteria for automatic checkpoint selection. By using these criteria, including visibility, stairs and clearness, we then implement this automation process. The key behind our technique is a lightweight, effective algorithm using left‐hand‐side and right‐hand‐side objects for path occlusion detection, which benefits both automatic checkpoint selection and occlusion‐aware path annotation on selected image checkpoints. Our experimental results show that our automatic checkpoint selection method works well in different navigation scenarios. The quality of automatically selected checkpoints is comparable to that of manually selected ones and higher than that of checkpoints by alternative automatic methods.

We introduce inverted stippling , a method to mimic an inversion technique used by artists when performing stippling. To this end, we extend Linde-Buzo-Gray (LBG) stippling to multi-class LBG (MLBG) stippling with multiple layers. MLBG stippling couples the layers stochastically to optimize for per-layer and overall blue-noise properties. We propose a stipple-based filling method to generate solid color backgrounds for inverting areas. Our experiments demonstrate the effectiveness of MLBG in terms of reducing overlapping and intensity accuracy. In addition, we showcase MLBG with color stippling and dynamic multi-class blue-noise sampling, which is possible due to its support for temporal coherence.

Creating animated virtual AR characters closely interacting with real environments is interesting but difficult. Existing systems adopt video see-through approaches to indirectly control a virtual character in mobile AR, making close interaction with real environments not intuitive. In this work we use an AR-enabled mobile device to directly control the position and motion of a virtual character situated in a real environment. We conduct two guessability studies to elicit user-defined motions of a virtual character interacting with real environments, and a set of user-defined motion gestures describing specific character motions. We found that an SVM-based learning approach achieves reasonably high accuracy for gesture classification from the motion data of a mobile device. We present ARAnimator , which allows novice and casual animation users to directly represent a virtual character by an AR-enabled mobile phone and control its animation in AR scenes using motion gestures of the device, followed by animation preview and interactive editing through a video see-through interface. Our experimental results show that with ARAnimator , users are able to easily create in-situ character animations closely interacting with different real environments.

Mid-air 3D sketching has been mainly explored in Virtual Reality (VR) and typically requires special hardware for motion capture and immersive, stereoscopic displays. The recently developed motion tracking algorithms allow real-time tracking of mobile devices, and have enabled a few mobile applications for 3D sketching in Augmented Reality (AR). However, they are more suitable for making simple drawings only, since they do not consider special challenges with mobile AR 3D sketching, including the lack of stereo display, narrow field of view, and the coupling of 2D input, 3D input and display. To address these issues, we present Mobi3DSketch, which integrates multiple sources of inputs with tools, mainly different versions of 3D snapping and planar/curves surface proxies. Our multimodal interface supports both absolute and relative drawing, allowing easy creation of 3D concept designs in situ. The effectiveness and expressiveness of Mobi3DSketch are demonstrated via a pilot study.

Most graphics hardware features memory to store textures and vertex data for rendering. However, because of the irreversible trend of increasing complexity of scenes, rendering a scene can easily reach the limit of memory resources. Thus, vertex data are preferably compressed, with a requirement that they can be decompressed during rendering. In this paper, we present a novel method to exploit existing hardware texture compression circuits to facilitate the decompression of vertex data in graphics processing unit (GPUs). This built-in hardware allows real-time, random-order decoding of data. However, vertex data must be packed into textures, and careless packing arrangements can easily disrupt data coherence. Hence, we propose an optimization approach for the best vertex data permutation that minimizes compression error. All of these result in fast and high-quality vertex data decompression for real-time rendering. To further improve the visual quality, we introduce vertex clustering to reduce the dynamic range of data during quantization. Our experiments demonstrate the effectiveness of our method for various vertex data of 3D models during rendering with the advantages of a minimized memory footprint and high frame rate.

It is usual for a consumer to search a product based on its category and go to related kind of shop to buy a product, e.g. food in supermarket, a pencil from a stationary shop and etc. While it is not uncommon nowadays for a shop to sell various categories of goods at the same time, like a newspaper stand do sell toys, an accessory shop has stationary. However, consumer may not easily notice and purchase these goods, especially if they are in hurry or not familiar with the shops nearby. With the emergence and popularity of many shopping search engines (shopbots), we can actually provide a better matching between the consumer and seller. In this paper, we developed a shopbot app system (Where2Buy) on smartphone that can search and filter the nearby shops which sell the desired products. To simplify the input process, our system allows users to search by text or voice and fuzzy matching is supported to widen the scope of searching. Detailed information of related product and shops are displayed in the result, together with a navigation map showing the best route to the target shops. If the desired product is not available nearby, substitutes in the same category will be recommended for the users. Our user study evidences that our system is simply and easy to use. More than half of the participants prefer Where2Buy than the other available shopbots in Hong Kong.

This paper tackles a challenging 2D collage generation problem, focusing on shapes: we aim to fill a given region by packing irregular and reasonably-sized shapes with minimized gaps and overlaps. To achieve this nontrivial problem, we first have to analyze the boundary of individual shapes and then couple the shapes with partially-matched boundary to reduce gaps and overlaps in the collages. Second, the search space in identifying a good coupling of shapes is highly enormous, since arranging a shape in a collage involves a position, an orientation, and a scale factor. Yet, this matching step needs to be performed for every single shape when we pack it into a collage. Existing shape descriptors are simply infeasible for computation in a reasonable amount of time. To overcome this, we present a brand new, scale- and rotation-invariant 2D shape descriptor, namely pyramid of arclength descriptor (PAD). Its formulation is locally supported, scalable, and yet simple to construct and compute. These properties make PAD efficient for performing the partial-shape matching. Hence, we can prune away most search space with simple calculation, and efficiently identify candidate shapes. We evaluate our method using a large variety of shapes with different types and contours. Convincing collage results in terms of visual quality and time performance are obtained.