Oscar Kin-Chung Au

Skeleton extraction by mesh contraction

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 objects 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.

Dual Laplacian editing for meshes

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.

Bilateral Normal Filtering for Mesh Denoising

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.

Multitouch finger registration and its applications

We present a simple finger registration technique that can distinguish in real-time which hand and fingers of the user are touching the touchscreen. The finger registration process is activated whenever the user places a hand, in any orientation, anywhere on the touchscreen. Such a finger registration technique enables the design of intuitive multitouch interfaces that directly map different combinations of the users fingers to the interface operations. In this paper, we first study the effectiveness and robustness of the finger registration process. We then demonstrate the usability of our finger registration method for two new interfaces. Specifically, we describe the Palm Menu, which is an intuitive dynamic menu interface that minimizes hand and eye movement during operations, and a virtual mouse interface that enables user to perform mouse operations in multitouch environment. We conducted controlled experiments to compare the performance of the Palm Menu against common command selection interfaces and the virtual mouse against traditional pointing devices.

Component-wise Controllers for Structure-Preserving Shape Manipulation

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.

Electors Voting for Fast Automatic Shape Correspondence

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.

Mesh Segmentation with Concavity-Aware Fields

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.

Effective Derivation of Similarity Transformations for Implicit Laplacian Mesh Editing

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.

Two-Finger Gestures for 6DOF Manipulation of 3D Objects

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.

Multitouch Gestures for Constrained Transformation of 3D Objects

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.