Cloud-based Smart Point Cloud Processing (CSPCP)
Established in 1963, the R&D 100 Awards is the only S&T (science and technology) awards competition that recognizes new commercial products, technologies and materials for their technological significance that are available for sale or license. The R&D 100 Awards have long been a benchmark of excellence for industry sectors as diverse as telecommunications, high-energy physics, software, manufacturing, and biotechnology. This 2021 R&D 100 winner is listed below, along with its respective category.
Category: IT/Electrical
Developers: National Center for High-performance Computing (NCHC) of National Applied Research Laboratories (NARLabs) National Center for High-performance Computing (NCHC)
Co-Developers: National Yang Ming Chiao Tung University, Tunghai University
Taiwan (Greater China)
Product Description:CSPCP is the world’s first smart point cloud processing technology built on the cloud, which automatically corrects abnormal colors in point clouds and increases the rendered image quality through cloud services, accurately recreates digital three-dimensional (3D) scenes, and accelerates developments in historic preservation, visual production, construction inspections, and craniofacial reconstruction.
Developers: National Center for High-performance Computing (NCHC) of National Applied Research Laboratories (NARLabs) National Center for High-performance Computing (NCHC)
Co-Developers: National Yang Ming Chiao Tung University, Tunghai University
Taiwan (Greater China)
Product Description:CSPCP is the world’s first smart point cloud processing technology built on the cloud, which automatically corrects abnormal colors in point clouds and increases the rendered image quality through cloud services, accurately recreates digital three-dimensional (3D) scenes, and accelerates developments in historic preservation, visual production, construction inspections, and craniofacial reconstruction.
Correction for superimposition: an accelerated solution for abnormal point clouds