Shenzhen VisuCA Key Laboratory SIAT Shenzhen China

China

Shenzhen VisuCA Key Laboratory SIAT Shenzhen China

China

Time filter

Source Type

Yin K.,Shenzhen VisuCA Key Laboratory SIAT Shenzhen China | Huang H.,Shenzhen VisuCA Key Laboratory SIAT Shenzhen China | Long P.,Shenzhen VisuCA Key Laboratory SIAT Shenzhen China | Gaissinski A.,Tel Aviv University | And 2 more authors.
Computer Graphics Forum | Year: 2015

Digitally capturing vegetation using off-the-shelf scanners is a challenging problem. Plants typically exhibit large self-occlusions and thin structures which cannot be properly scanned. Furthermore, plants are essentially dynamic, deforming over the time, which yield additional difficulties in the scanning process. In this paper, we present a novel technique for acquiring and modelling of plants and foliage. At the core of our method is an intrusive acquisition approach, which disassembles the plant into disjoint parts that can be accurately scanned and reconstructed offline. We use the reconstructed part meshes as 3D proxies for the reconstruction of the complete plant and devise a global-to-local non-rigid registration technique that preserves specific plant characteristics. Our method is tested on plants of various styles, appearances and characteristics. Results show successful reconstructions with high accuracy with respect to the acquired data. © 2015 The Eurographics Association and John Wiley & Sons Ltd.


Wang Y.H.,Shenzhen VisuCA Key Laboratory SIAT Shenzhen China | Fan C.R.,East China Normal University | Zhang J.,Chinese Academy of Sciences | Niu T.,Chinese Academy of Meteorological Sciences | And 2 more authors.
Computer Graphics Forum | Year: 2014

Precipitation forecast verification is essential to the quality of a forecast. The Gaussian mixture model (GMM) can be used to approximate the precipitation of several rain bands and provide a concise view of the data, which is especially useful for comparing forecast and observation data. The robustness of such comparison mainly depends on the consistency of and the correspondence between the extracted rain bands in the forecast and observation data. We propose a novel co-estimation approach based on GMM in which forecast and observation data are analysed simultaneously. This approach naturally increases the consistency of and correspondence between the extracted rain bands by exploiting the similarity between both forecast and observation data. Moreover, a novel visualization and exploration framework is implemented to help the meteorologists gain insight from the forecast. The proposed approach was applied to the forecast and observation data provided by the China Meteorological Administration. The results are evaluated by meteorologists and novel insight has been gained. © 2014 The Eurographics Association and John Wiley & Sons Ltd.

Loading Shenzhen VisuCA Key Laboratory SIAT Shenzhen China collaborators
Loading Shenzhen VisuCA Key Laboratory SIAT Shenzhen China collaborators