To solve the problem of streetlamp extraction and category identification from mobile LiDAR data in urban scenes, an algorithm based on streetlamp sample has been raised. Firstly, based on morphological closing operation to extract the location of suspected streetlamps; secondly, determine the scope of suspected streetlamps by the streetlamp sample parameters and extract the suspected streetlamp point cloud; thirdly, through pole and head matching to realize the alignment of streetlamp samples and suspected streetlamp point cloud; finally, realize the streetlamp judgment and extraction by establishing sample buffer. Experimental results show that the algorithm cannot only automatically extract streetlamp point cloud, but also be able to identify streetlamp category which is in accordance with the sample, achieving the rapid streetlamp extraction and recognition.

Summary Parallel semi‐implicit method for pressure‐linked equations(SIMPLE) algorithm is used to solve the 3‐D incompressible pipe flow problem. In this paper, we proposed a novel parallel SIMPLE algorithm that uses the alternate tiling technique. Firstly, a parallel SIMPLE algorithm based on domain decomposition method was established, and the implementation of domain partition and data exchange was presented. Then, we presented serial finite difference stencil algorithm based on alternate tiling. Furthermore, an iteration space parallel two‐way finite difference stencil algorithm based on alternate tiling was proposed, introducing the sequence of iterative space tiles as the sequence of execution and using time skewing technique to partition the iteration space, thus to improve the data locality of algorithm. The cache misses and the cost of communication and synchronization are reduced by reordering the tiles of iteration space. Finally, the effectiveness of the two parallel SIMPLE algorithms were compared. The results showed that the parallel SIMPLE algorithm that uses the two‐way finite difference stencil algorithm based on alternate tiling has good data locality, performance, and scalability in the Deepcomp7000 cluster computing environment. Copyright © 2013 John Wiley & Sons, Ltd.

We present a real-time solution for generating detailed clothing deformations from pre-computed clothing shape examples. Given an input pose, it synthesizes a clothing deformation by blending skinned clothing deformations of nearby examples controlled by the body skeleton. Observing that cloth deformation can be well modeled with sensitivity analysis driven by the underlying skeleton, we introduce a sensitivity based method to construct a pose-dependent rigging solution from sparse examples. We also develop a sensitivity based blending scheme to find nearby examples for the input pose and evaluate their contributions to the result. Finally, we propose a stochastic optimization based greedy scheme for sampling the pose space and generating example clothing shapes. Our solution is fast, compact and can generate realistic clothing animation results for various kinds of clothes in real time.

SIMPLE (Semi-Implicit Method for Pressure-Linked Equations) algorithm is important in the simulation of steady flows. As the traditional 3-D SIMPLE algorithm is time-consuming, we propose a parallel SIMPLE algorithm based on a novel tiling strategy -- alternate tiling, through replacing the original linear system and reordering the iteration space tiles. The novelty of our parallel algorithm lies in the introduction of the sequence of iteration space tiles as the sequence of execution, the time skewing technique to partition the iteration space, update operations of the grids from two directions alternately, and the improvement of the data locality. The effectiveness of the parallel algorithm and serial model of finite difference stencil algorithm are validated. Numerical experiments on distributed clusters show that the cache misses and the cost of communication and synchronization are reduced by reordering the tiles of iteration space, and the parallel SIMPLE algorithm based on alternate tiling has a good data locality and parallel efficiency in the three-dimensional incompressible pipe flow project.