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Volume 6 No. 1
05 March 2020

Shi-Min Hu

2020, 6(1): 1-1.   doi:10.1007/s41095-020-0170-z
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Miao Wang, Xu-Quan Lyu, Yi-Jun Li, Fang-Lue Zhang

2020, 6(1): 3-28.   doi:10.1007/s41095-020-0162-z
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Virtual reality (VR) offers an artificial, com-puter generated simulation of a real life environment. It originated in the 1960s and has evolved to provide increasing immersion, interactivity, imagination, and intelligence. Because deep learning systems are able to represent and compose information at various levels in a deep hierarchical fashion, they can build very powerful models which leverage large quantities of visual media data. Intelligence of VR methods and applications has been sign...

Tomoya Yamaguchi, Tatsuya Yatagawa, Yusuke Tokuyoshi, Shigeo Morishima

2020, 6(1): 29-36.   doi:10.1007/s41095-019-0154-z
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This paper proposes a lightweight bi-directional scattering distribution function (BSDF) model for layered materials with anisotropic reflection and refraction properties. In our method, each layer of the materials can be described by a microfacet BSDF using an anisotropic normal distribution function (NDF). Furthermore, the NDFs of layers can be defined on tangent vector fields, which differ from layer to layer. Our method is based on a previous study in which isotropic BSDFs are approximate...

Hong Deng, Beibei Wang, Rui Wang, Nicolas Holzschuch

2020, 6(1): 37-51.   doi:10.1007/s41095-020-0160-1
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Rendering translucent materials is costly: light transport algorithms need to simulate a large number of scattering events inside the material before reaching convergence. The cost is especially high for materials with a large albedo or a small mean-free-path, where higher-order scattering effects dominate. In simple terms, the paths get lost in the medium. Path guiding has been proposed for surface rendering to make convergence faster by guiding the sampling process. In this paper, we introd...

Lachlan J. Deakin, Mark A. Knackstedt

2020, 6(1): 53-63.   doi:10.1007/s41095-019-0155-y
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Volume and isosurface rendering are methods of projecting volumetric images to two dimensions for visualisation. These methods are common in medical imaging and scientific visualisation.

Head-mounted optical see-through displays have recently become an affordable technology and are a promising platform for volumetric image visualisation. Images displayed on a head-mounted display must be presented at a high frame rate and with low latency to compensate for head motion. High latency can ...

Yongqing Liang, Navid Jafari, Xing Luo, Qin Chen, Yanpeng Cao, Xin Li

2020, 6(1): 65-78.   doi:10.1007/s41095-020-0156-x
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We develop a novel network to segment water with significant appearance variation in videos. Unlike existing state-of-the-art video segmentation approaches that use a pre-trained feature recognition network and several previous frames to guide seg-mentation, we accommodate the object’s appearance variation by considering features observed from the current frame. When dealing with segmentation of objects such as water, whose appearance is non-uniform and changing dynamically, our pipeline can ...

Song-Hai Zhang, Zheng-Ping Zhou, Bin Liu, Xi Dong, Peter Hall

2020, 6(1): 79-93.   doi:10.1007/s41095-020-0158-8
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We propose a novel problem revolving around two tasks: (i) given a scene, recommend objects to insert, and (ii) given an object category, retrieve suitable background scenes. A bounding box for the inserted object is predicted in both tasks, which helps downstream applications such as semi-automated advertising and video composition. The major challenge lies in the fact that the target object is neither present nor localized in the input, and furthermore, available datasets only provide scene...

Jianwei Guo, Hanyu Wang, Zhanglin Cheng, Xiaopeng Zhang, Dong-Ming Yan

2020, 6(1): 95-112.   doi:10.1007/s41095-020-0163-y
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A discriminative local shape descriptor plays an important role in various applications. In this paper, we present a novel deep learning framework that derives discriminative local descriptors for deformable 3D shapes. We use local "geometry images" to encode the multi-scale local features of a point, via an intrinsic parameterization method based on geodesic polar coordinates. This new parameterization provides robust geometry images even for badly-shaped triangular meshes. Then a ...