This paper presents a novel tensor-based representation, namely hierarchical multiway K-clustered tensor approximation, for multidimensional visual datasets. The proposed method extends a previous tensor model [20] into a hierarchical representation that can significantly reduce offline computational cost as well as provide similar approximation quality and rendering performance at the same time. We also apply the proposed method to approximate spatially-varying bidirectional reflectance distribution functions, time-varying light fields, and time-varying volume data to show its effectiveness and potential for data-driven rendering. Experimental results demonstrate that under similar performance to previous work, the proposed method can reduce offline approximation time by even an order of magnitude.

Text entry remains challenging in virtual environments, where users may quickly experience physical fatigue in some body parts using existing methods. In this paper, we propose "CrowbarLimbs," a novel virtual reality (VR) text entry metaphor with two deformable extended virtual limbs. By using a crowbar-like metaphor and placing the virtual keyboard at a user-preferred location based on the user's physical stature, our method can assist the user in placing their hands and arms in a comfortable posture, thus effectively reducing the physical fatigue in various body parts, such as hands, wrists, and elbows. In an initial user study, we found that CrowbarLimbs achieved text entry speed, accuracy, and system usability comparable to those of previous VR typing methods. To investigate the proposed metaphor in more depth, we further conducted two additional user studies to explore the ergonomically user-friendly shapes of CrowbarLimbs and virtual keyboard locations. The experimental results indicate that the shapes of CrowbarLimbs have significant effects on the fatigue ratings in various body parts and text entry speed. Furthermore, placing the virtual keyboard near the user and at half their height can lead to a satisfactory text entry rate of 28.37 words per minute.

This article presents a generalized sparse multilinear model, namely multiway K-clustered tensor approximation (MK-CTA), for synthesizing photorealistic 3D images from large-scale multidimensional visual datasets. MK-CTA extends previous tensor approximation algorithms, particularly K-clustered tensor approximation (K-CTA) [Tsai and Shih 2012], to partition a multidimensional dataset along more than one dimension into overlapped clusters. On the contrary, K-CTA only sparsely clusters a dataset along just one dimension and often fails to efficiently approximate other unclustered dimensions. By generalizing K-CTA with multiway sparse clustering, MK-CTA can be regarded as a novel sparse tensor-based model that simultaneously exploits the intra- and inter-cluster coherence among different dimensions of an input dataset. Our experiments demonstrate that MK-CTA can accurately and compactly represent various multidimensional datasets with complex and sharp visual features, including bidirectional texture functions (BTFs) [Dana et al. 1999], time-varying light fields (TVLFs) [Bando et al. 2013], and time-varying volume data (TVVD) [Wang et al. 2010], while easily achieving high rendering rates in practical graphics applications.

Abstract This paper presents a novel example‐based stippling technique that employs a simple and intuitive concept to convert a color image into a pointillism painting. Our method relies on analyzing and imitating the color distributions of Seurat's paintings to obtain a statistical color model. Then, this model can be easily combined with the modified multi‐class blue noise sampling to stylize an input image with characteristics of color composition in Seurat's paintings. The blue noise property of the output image also ensures that the color points are randomly located but remain spatially uniform. In our experiments, the multivariate goodness‐of‐fit tests were adopted to quantitatively analyze the results of the proposed and previous methods, further confirming that the color composition of our results are more similar to Seurat's painting style than that of previous approaches. Additionally, we also conducted a user study participated by artists to qualitatively evaluate the synthesized images of the proposed method.

Ray marching is an important technique to generate volumetric lighting effects. However, it is very expensive for each pixel on the screen, especially in dynamic scenes. The authors propose an adaptive approach to reduce samples for volumetric shadows in the time domain. In dynamic scenes, shadow volumes of moving objects are created and are rasterised to decide pixels that cannot be reused. The authors use a stencil buffer to maintain the information of screen pixels by recomputing or just using the previous information. Experimental results show that the proposed approach is simple to implement. Moreover, compared to the previous method, the proposed approach achieves a specific speedup and maintains similar visual quality.

With the increasing demands for photo-realistic image synthesis in real time, we propose a sparse multilinear model, which is named K-Clustered Tensor Approximation (K-CTA), to efficiently analyze and approximate large-scale multidimensional visual datasets, so that both storage space and rendering time are substantially reduced. K-CTA not only extends previous work on Clustered Tensor Approximation (CTA) to exploit inter-cluster coherence, but also allows a compact and sparse representation for high-dimensional datasets with just a few low-order factors and reduced multidimensional cluster core tensors. Thus, K-CTA can be regarded as a sparse extension of CTA and a multilinear generalization of sparse representation. Experimental results demonstrate that K-CTA can accurately approximate spatially varying visual datasets, such as bidirectional texture functions, view-dependent occlusion texture functions, and biscale radiance transfer functions for efficient rendering in real-time applications.

This paper presents a novel parametric representation for bidirectional texture functions. Our method mainly relies on two original techniques, namely, multivariate spherical radial basis functions (SRBFs) and optimized parameterization. First, since the surface appearance of a real-world object is frequently a mixed effect of different physical factors, the proposed sum-of-products model based on multivariate SRBFs especially provides an intrinsic and efficient representation for heterogenous materials. Second, optimized parameterization particularly aims at overcoming the major disadvantage of traditional fixed parameterization. By using a parametric model to account for variable transformations, the parameterization process can be tightly integrated with multivariate SRBFs into a unified framework. Finally, a hierarchical fitting algorithm for bidirectional texture functions is developed to exploit spatial coherence and reduce computational cost. Our experimental results further reveal that the proposed representation can easily achieve high-quality approximation and real-time rendering performance.

This paper introduces a new data representation and compression technique for precomputed radiance transfer (PRT). The light transfer functions and light sources are modeled with spherical radial basis functions (SRBFs). A SRBF is a rotation-invariant function that depends on the geodesic distance between two points on the unit sphere. Rotating functions in SRBF representation is as straightforward as rotating the centers of SRBFs. Moreover, high-frequency signals are handled by adjusting the bandwidth parameters of SRBFs. To exploit inter-vertex coherence, the light transfer functions are further classified iteratively into disjoint clusters, and tensor approximation is applied within each cluster. Compared with previous methods, the proposed approach enables real-time rendering with comparable quality under high-frequency lighting environments. The data storage is also more compact than previous all-frequency PRT algorithms.