In this work we introduce 2D-Dragger, a unified touch-based target acquisition technique that enables easy access to small targets in dense regions or distant targets on screens of various sizes. The effective width of a target is constant with our tool, allowing a fixed scale of finger movement for capturing a new target. Our tool is thus insensitive to the distribution and size of the selectable targets, and consistently works well for screens of different sizes, from mobile to wall-sized screens. Our user studies show that overall 2D-Dragger performs the best compared to the state-of-the-art techniques for selecting both near and distant targets of various sizes in different densities.

Touch input is increasingly popular nowadays, especially for mobile devices such as smartphones and tablet computers. However, the human finger has considerably large fingertip size and finger input is imprecise. As such, acquiring small targets on a touch screen is still a challenging task. In this paper, we present the LinearDragger, a new and integrated one-finger target acquisition technique for small and clustered targets. The proposed method has three advantages. First, it allows users to select targets in dense clustered groups easily with a single touch-drag-release operation. Second, it maps the 2D selection problem into a more precise 1D selection problem, which is independent of the target distribution. Third, it avoids finger occlusion and does not create visual distraction. As a result, it is particularly suitable for applications with dense targets and rich visual elements. Results of our controlled experiments show that when selecting small targets, LinearDragger takes about 70% and 30% less selection time than target acquisition without using any techniques and with the state-of-the-art target acquisition technique that involves a single touch operation, respectively, while maintaining a reasonable error rate.

We present the Implicit Fan Cursor (IFC) - a novel target pointing technique using a cursor with a fan-shape activation area. The IFC couples the cursor's activation area with its velocity, i.e., the speed and direction of the mouse motion, behaving like a 2D spotlight cursor at low speed and a circular area cursor at high speed. Thus, it enables the user to precisely acquire distant targets at low speed and easily acquire nearest targets at high speed, without explicit mode switching. This technique minimizes cursor movement, while taking into consideration of the precision of cursor movement at different speeds. It also ensures that only one target is captured at any time. The results of our controlled experiments show that the IFC outperforms the point cursor and the area cursor techniques, particularly in terms of cursor moving distance, and that its performance can be accurately modeled using the Fitts' law.

Abstract 3D transformation widgets allow constrained manipulations of 3D objects and are commonly used in many 3D applications for fine‐grained manipulations. Since traditional transformation widgets have been mainly designed for mouse‐based systems, they are not user friendly for multitouch screens. There is little research on how to use the extra input bandwidth of multitouch screens to ease constrained transformation of 3D objects. This paper presents a small set of multitouch gestures which offers a seamless control of manipulation constraints (i.e., axis or plane) and modes (i.e., translation, rotation or scaling). Our technique does not require any complex manipulation widgets but candidate axes, which are for visualization rather than direct manipulation. Such design not only minimizes visual clutter but also tolerates imprecise touch‐based inputs. To further expand our axis‐based interaction vocabulary, we introduce intuitive touch gestures for relative manipulations, including snapping and borrowing axes of another object. A preliminary evaluation shows that our technique is more effective than a direct adaption of standard transformation widgets to the tactile paradigm.

Abstract Multitouch input devices afford effective solutions for 6DOF (six Degrees of Freedom) manipulation of 3D objects. Mainly focusing on large‐size multitouch screens, existing solutions typically require at least three fingers and bimanual interaction for full 6DOF manipulation. However, single‐hand, two‐finger operations are preferred especially for portable multitouch devices (e.g., popular smartphones) to cause less hand occlusion and relieve the other hand for necessary tasks like holding the devices. Our key idea for full 6DOF control using only two contact fingers is to introduce two manipulation modes and two corresponding gestures by examining the moving characteristics of the two fingers, instead of the number of fingers or the directness of individual fingers as done in previous works. We solve the resulting binary classification problem using a learning‐based approach. Our pilot experiment shows that with only two contact fingers and typically unimanual interaction, our technique is comparable to or even better than the state‐of‐the‐art techniques.

This paper presents Lazy Selection , a scribble-based tool for quick selection of one or more desired shape elements by roughly stroking through the elements. Our algorithm automatically refines the selection and reveals the user's intention. To give the user maximum flexibility but least ambiguity, our technique first extracts selection candidates from the scribble-covered elements by examining the underlying patterns and then ranks them based on their location and shape with respect to the user-sketched scribble. Such a design makes our tool tolerant to imprecise input systems and applicable to touch systems without suffering from the fat finger problem. A preliminary evaluation shows that compared to the standard click and lasso selection tools, which are the most commonly used, our technique provides significant improvements in efficiency and flexibility for many selection scenarios.

This paper presents a simple and efficient automatic mesh segmentation algorithm that solely exploits the shape concavity information. The method locates concave creases and seams using a set of concavity-sensitive scalar fields. These fields are computed by solving a Laplacian system with a novel concavity-sensitive weighting scheme. Isolines sampled from the concavity-aware fields naturally gather at concave seams, serving as good cutting boundary candidates. In addition, the fields provide sufficient information allowing efficient evaluation of the candidate cuts. We perform a summarization of all field gradient magnitudes to define a score for each isoline and employ a score-based greedy algorithm to select the best cuts. Extensive experiments and quantitative analysis have shown that the quality of our segmentations are better than or comparable with existing state-of-the-art more complex approaches.

This paper presents a very easy-to-use interactive tool, which we call dot scissor, for mesh segmentation. The user's effort is reduced to placing only a single click where a cut is desired. Such a simple interface is made possible by a directional search strategy supported by a concavity-aware harmonic field and a robust voting scheme that selects the best isoline as the cut. With a concavity-aware weighting scheme, the harmonic fields gather dense isolines along concave regions which are natural boundaries of semantic components. The voting scheme relies on an isoline-face scoring mechanism that considers both shape geometry and user intent. We show by extensive experiments and quantitative analysis that our tool advances the state-of-the-art segmentation methods in both simplicity of use and segmentation quality.

This paper presents four research projects currently underway to develop new omnispatial visualization strategies for the collaborative interrogation of large-scale heterogeneous cultural datasets using the worlds' first 360-degree stereoscopic visualization environment (Advanced Visualization and Interaction Environment - AVIE). The AVIE system enables visualization modalities through full body immersion, stereoscopy, spatialized sound and camera-based tracking. The research integrates work by a group of international investigators in virtual environment design, immersive interactivity, information visualization, museology, visual analytics and computational linguistics. The work is being implemented at the newly established research facility, City University's Applied Laboratory for Interactive Visualization and Embodiment -- ALIVE) in association with partners Museum Victoria (Melbourne), iCinema Centre, UNSW (Sydney), ZKM Centre for Art and Media (Karlsruhe), UC Berkeley (USA), UC Merced (USA) and and Europeana (in association with Israel Museum of Jerusalem). The applications are intended for museum visitors and for humanities researchers. They are: (1) Data Sculpture Museum, (2) Inside Europeana, (2) Rhizome of the Western Han, (4) Blue Dots AVIE (Tripitaka Koreana).

Abstract Recent shape editing techniques, especially for man‐made models, have gradually shifted focus from maintaining local, low‐level geometric features to preserving structural, high‐level characteristics like symmetry and parallelism. Such new editing goals typically require a pre‐processing shape analysis step to enable subsequent shape editing. Observing that most editing of shapes involves manipulating their constituent components, we introduce component‐wise controllers that are adapted to the component characteristics inferred from shape analysis. The controllers capture the natural degrees of freedom of individual components and thus provide an intuitive user interface for editing. A typical model usually results in a moderate number of controllers, allowing easy establishment of semantic relations among them by automatic shape analysis supplemented with user interaction. We propose a component‐wise propagation algorithm to automatically preserve the established inter‐relations while maintaining the defining characteristics of individual controllers and respecting the user‐specified modeling constraints. We extend these ideas to a hierarchical setup, allowing the user to adjust the tool complexity with respect to the desired modeling complexity. We demonstrate the effectiveness of our technique on a wide range of man‐made models with structural features, often containing multiple connected pieces.

Decoupling local geometric features from the spatial location of a mesh is crucial for feature-preserving mesh denoising. This paper focuses on first order features, i.e., facet normals, and presents a simple yet effective anisotropic mesh denoising framework via normal field denoising. Unlike previous denoising methods based on normal filtering, which process normals defined on the Gauss sphere, our method considers normals as a surface signal defined over the original mesh. This allows the design of a novel bilateral normal filter that depends on both spatial distance and signal distance. Our bilateral filter is a more natural extension of the elegant bilateral filter for image denoising than those used in previous bilateral mesh denoising methods. Besides applying this bilateral normal filter in a local, iterative scheme, as common in most of previous works, we present for the first time a global, noniterative scheme for an isotropic denoising. We show that the former scheme is faster and more effective for denoising extremely noisy meshes while the latter scheme is more robust to irregular surface sampling. We demonstrate that both our feature-preserving schemes generally produce visually and numerically better denoising results than previous methods, especially at challenging regions with sharp features or irregular sampling.

Abstract This paper challenges the difficult problem of automatic semantic correspondence between two given shapes which are semantically similar but possibly geometrically very different (e.g., a dog and an elephant). We argue that the challenging part is the establishment of a sparse correspondence and show that it can be efficiently solved by considering the underlying skeletons augmented with intrinsic surface information. To avoid potentially costly direct search for the best combinatorial match between two sets of skeletal feature nodes, we introduce a statistical correspondence algorithm based on a novel voting scheme, which we call electors voting. The electors are a rather large set of correspondences which then vote to synthesize the final correspondence. The electors are selected via a combinatorial search with pruning tests designed to quickly filter out a vast majority of bad correspondence. This voting scheme is both efficient and insensitive to parameter and threshold settings. The effectiveness of the method is validated by precision‐recall statistics with respect to manually defined ground truth. We show that high quality correspondences can be instantaneously established for a wide variety of model pairs, which may have different poses, surface details, and only partial semantic correspondence.

Extraction of curve-skeletons is a fundamental problem with many applications in computer graphics and visualization. In this paper, we present a simple and robust skeleton extraction method based on mesh contraction. The method works directly on the mesh domain, without pre-sampling the mesh model into a volumetric representation. The method first contracts the mesh geometry into zero-volume skeletal shape by applying implicit Laplacian smoothing with global positional constraints. The contraction does not alter the mesh connectivity and retains the key features of the original mesh. The contracted mesh is then converted into a 1D curve-skeleton through a connectivity surgery process to remove all the collapsed faces while preserving the shape of the contracted mesh and the original topology. The centeredness of the skeleton is refined by exploiting the induced skeleton-mesh mapping. In addition to producing a curve skeleton, the method generates other valuable information about the object's geometry, in particular, the skeleton-vertex correspondence and the local thickness, which are useful for various applications. We demonstrate its effectiveness in mesh segmentation and skinning animation.

Existing Poisson mesh editing techniques mainly focus on designing schemes to propagate deformation from a given boundary condition to a region of interest. Although solving the Poisson system in the least-squares sense distributes the distortion errors over the entire region of interest, large deformation in the boundary condition might still lead to severely distorted results. We propose to optimize the boundary condition (the merging boundary) for Poisson mesh merging. The user needs only to casually mark a source region and a target region. Our algorithm automatically searches for an optimal boundary condition within the marked regions such that the change of the found boundary during merging is minimal in terms of similarity transformation. Experimental results demonstrate that our merging tool is easy to use and produces visually better merging results than unoptimized techniques.

Abstract Laplacian coordinates as a local shape descriptor have been employed in mesh editing. As they are encoded in the global coordinate system, they need to be transformed locally to reflect the changed local features of the deformed surface. We present a novel implicit Laplacian editing framework which is linear and effectively captures local rotation information during editing. Directly representing rotation with respect to vertex positions in 3D space leads to a nonlinear system. Instead, we first compute the affine transformations implicitly defined for all the Laplacian coordinates by solving a large sparse linear system, and then extract the rotation and uniform scaling information from each solved affine transformation. Unlike existing differential‐based mesh editing techniques, our method produces visually pleasing deformation results under large angle rotations or big‐scale translations of handles. Additionally, to demonstrate the advantage of our editing framework, we introduce a new intuitive editing technique, called configuration‐independent merging, which produces the same merging result independent of the relative position, orientation, scale of input meshes.

Handle-based mesh deformation is essentially a nonlinear problem. To allow scalability, the original deformation problem can be approximately represented by a compact set of control variables. We show the direct relation between the locations of handles on the mesh and the local rigidity under deformation, and introduce the notion of handle-aware rigidity . Then, we present a reduced model whose control variables are intelligently distributed across the surface, respecting the rigidity information and the geometry. Specifically, for each handle, the control variables are the transformations of the isolines of a harmonic scalar field representing the deformation propagation from that handle. The isolines constitute a virtual skeletal structure similar to the bones in skinning deformation, thus correctly capturing the low-frequency shape deformation. To interpolate the transformations from the isolines to the original mesh, we design a method which is local, linear and geometry-dependent. This novel interpolation scheme and the transformation-based reduced domain allow each iteration of the nonlinear solver to be fully computed over the reduced domain. This makes the per-iteration cost dependent on only the number of isolines and enables compelling deformation of highly detailed shapes at interactive rates. In addition, we show how the handle-driven isolines provide an efficient means for deformation transfer without full shape correspondence.

Recently, differential information as local intrinsic feature descriptors has been used for mesh editing. Given certain user input as constraints, a deformed mesh is reconstructed by minimizing the changes in the differential information. Since the differential information is encoded in a global coordinate system, it must somehow be transformed to fit the orientations of details in the deformed surface, otherwise distortion will appear. We observe that visually pleasing deformed meshes should preserve both local parameterization and geometry details. We propose to encode these two types of information in the dual mesh domain due to the simplicity of the neighborhood structure of dual mesh vertices. Both sets of information are nondirectional and nonlinearly dependent on the vertex positions. Thus, we present a novel editing framework that iteratively updates both the primal vertex positions and the dual Laplacian coordinates to progressively reduce distortion in parametrization and geometry. Unlike previous related work, our method can produce visually pleasing deformations with simple user interaction, requiring only the handle positions, not local frames at the handles.