TuioSimulator_1.4

说明：  一个初学D3D的Demo,有望高手给点意见(D3D a beginner)

三维重建是指对三维物体建立适合计算机表示和处理的数学模型,是在计算机环境下对其进行处理、操作和分析其性质的基础,也是在计算机中建立表达客观世界的虚拟现实的关键技术。(Three-dimensional reconstruction refers to the establishment of a mathematical model suitable for computer representation and processing of three-dimensional objects. It is the basis for processing, manipulating and analyzing their properties in the computer environment. It is also the key technology for establishing a virtual reality to express the objective world in the computer.)

说明：  三维点云的伪彩色显示示例。自带5片点云用于测试。可平移、旋转、缩放视图；可单色、伪彩色显示点云。(An example of pseudo-color display of three-dimensional point clouds. It has five point clouds for testing. It can translate, rotate and zoom views; it can display point clouds in monochrome and pseudo-color.)

一个用VC6++写的飞机模型，实现用键盘控制其方向，前进，是初学OPENGL的一个很好的例子(An to write with VC6++ aircraft model, use the keyboard to control the direction, forward, is the beginner the OPENGL of a good example)

相机在轨道上沿轨道（管状）运动； 右键可以选择不同视角观察和退出，有两个视角；(Camera on the track along the orbit (tubular) movement Right you can choose a different perspective and exit, there are two perspectives )

迭代最近点点云配准算法的matlab实现，最经典的算法(Iterative Closest little cloud registration algorithm matlab realize, the most classical algorithm)

一个火车三维虚拟场景,用户可以自行设计轨道、车厢，并可开动、停止火车。(Train a three-dimensional virtual scene, the user can design their own track, car, and start and stop the train.)

matlab点云三维重建，用matlab写成的，效果很好，而且速度也比较快(Matlab point cloud three-dimensional reconstruction, written in MATLAB, the effect is very good, and the speed is faster.)

一个比例和方向自适应均值漂移跟踪算法（SOAMST） 提出本文所要解决的问题，如何估计的规模和方向 改变均值漂移下的目标跟踪框架。在原来的均值偏移 跟踪算法，可以很好地估计目标的位置，规模的同时， 方向的变化，不能自适应估计。考虑到图像（重量） 是来自于目标运动模型和候选模型可以代表的可能性，一个 像素属于目标，我们证明了原来的均值漂移跟踪算法可以 推导出的重量图像的零阶和一阶矩。随着零阶 矩和目标模型和候选模型之间的Bhattacharyya系数， 提出了简单而有效的方法来估计的规模为目标。然后一种方法， 利用估计的区域和第二阶中心矩，提出 自适应地估计目标的宽度，高度和方向的变化。广泛 实验来证实所提出的方法，并验证其可靠性 规模和方向变化的目标。(A scale and orientation adaptive mean shift tracking (SOAMST) algorithm is proposed in this paper to address the problem of how to estimate the scale and orientation changes of the target under the mean shift tracking framework. In the original mean shift tracking algorithm, the position of the target can be well estimated, while the scale and orientation changes can not be adaptively estimated. Considering that the weight image derived from the target model and the candidate model can represent the possibility that a pixel belongs to the target, we show that the original mean shift tracking algorithm can be derived using the zeroth and the first order moments of the weight image. With the zeroth order moment and the Bhattacharyya coefficient between the target model and candidate model, a simple and effective method is proposed to estimate the scale of target. Then an approach, which utilizes the estimated area and the second order center moment, is proposed to adaptively e)

GPU编程教程,三维编程 GPU编程教程,三维编程(The GPU programming tutorial, three-dimensional programming)