三维重建KInectFusion 学习笔记

KInectFusion实战笔记有icp

步骤和条件：

从零开始复现实时三维重建KInectFusion

KInectFusion实战笔记有icp

KinectFusion实战笔记_kinectfusion python_Stefanie_Song的博客-CSDN博客

GitHub - JingwenWang95/KinectFusion: KinectFusion implemented in Python with PyTorch

步骤和条件：

1.下载数据：freiburg1_desk

2.执行dataset/preprocess.py

然后可以执行kinfu_gui.py

除了重建必要的深度图和彩色图之外，TUM数据集也包含相机轨迹的groundtruth，即在一个固定坐标系下，每一帧图像拍摄时相机的位置和方向坐标。

原文链接：https://blog.csdn.net/weixin_43272109/article/details/127099847

需要rgb图，深度图，

depth.txt和groundtruth.txt

实时三维重建KInectFusion c++

一，从零开始复现实时三维重建KInectFusion
实时三维重建算法
【[本文代码](https://github.com/sky3139/KinectFusion_2022)】
准备环境
准备数据集
检查数据集
计算相机坐标系下的点云
创建一个Patch管理GPU的2D内存
TSDF类
修改后的主函数
最后效果如下
实时三维重建算法
静态：
知乎：三维重建 3D reconstruction 有哪些实用算法？

【本文代码】
准备环境
我们对Markdown编辑器进行了一些功能拓展与语法支持，除了标准的Markdown编辑器功能，我们增加了如下几点新功能，帮助你用它写博客：

OpenCV：加上viz编译，方便可视化其他类似的可视化工具 pangolin opengl pcl qglviewer vtk open3d等；
CUDA 8.0 以上 KinectFusion 的复现工作大多基于cuda8.0 在ubuntu20.04 上难以工作，本次复现采用较新的api;
准备数据集
TUM数据集,这里以 f3_long_office为例子
将深度图和RGB图时间同步（后期更新此工具）
检查数据集

#include "read.hpp"
using namespace std;
int main()
{DataSet<float> dt("/home/lei/dataset/paper/f3_long_office");for (int i = 0; i < dt.pose.frames; i++){cv::Mat rgb = cv::imread(dt.color_path[i]);cv::Mat depth = cv::imread(dt.depth_path[i]);cv::imshow("rgb", rgb);cv::imshow("depth", depth);cv::waitKey(1);}
}

能正确读取rgb图和深度图即可

计算相机坐标系下的点云
定义一个内参结构体

struct Intr
{
union
{
float4 cam;
struct
{
float fx, fy, cx, cy;
};
};
Intr(float4 intr)
{
cam = intr;
}
__device__ inline float3 cam2world(int u, int v, float z)
{
// TODO 这里可预先计算fx，fy倒数，然后计算乘法，应该会快一些
float x = __fdividef(z * (u - cx), fx);
float y = __fdividef(z * (v - cy), fy);
return make_float3(x, y, z);
}
};

深度图转点云的cuda核函数

__global__ void depth2cam(uint16_t *hd_depth_ptr, float3 *output, Intr intr)
{
int tx = threadIdx.x; // 640
int ty = blockIdx.x; // 480
int rawidx = tx + ty * 640;
float pz = hd_depth_ptr[rawidx] * 0.0002f; //深度转换为米
output[rawidx] = intr.cam2world(tx, ty, pz);
}

main中

int main()
{DataSet<float> dt("/home/lei/dataset/paper/f3_long_office");ck(cudaGetLastError());Intr intr(make_float4(550, 550, 320, 240));cv::viz::Viz3d window("map");window.showWidget("Coordinate", cv::viz::WCoordinateSystem());for (int i = 0; i < dt.pose.frames; i++){cv::Mat rgb = cv::imread(dt.color_path[i]);cv::Mat depth = cv::imread(dt.depth_path[i], cv::IMREAD_ANYDEPTH);uint16_t *hd_depth_ptr;cudaMallocManaged((void **)&hd_depth_ptr, depth.rows * depth.cols * sizeof(uint16_t));cudaMemcpy(hd_depth_ptr, depth.ptr<uint16_t>(), depth.rows * depth.cols * sizeof(uint16_t), cudaMemcpyHostToDevice);float3 *cloud;cudaMallocManaged(&cloud, depth.rows * depth.cols * sizeof(float3));depth2cam<<<depth.rows, depth.cols>>>(hd_depth_ptr, cloud, intr);ck(cudaDeviceSynchronize());cv::Mat cpu_cloud(depth.rows * depth.cols, 1, CV_32FC3);cudaMemcpy(cpu_cloud.ptr<float *>(), cloud, depth.rows * depth.cols * sizeof(float3), cudaMemcpyDeviceToHost);cv::Mat dst2 = rgb.reshape(3,640 * 480);window.showWidget("depthmode", cv::viz::WCloud(cpu_cloud, dst2));window.spinOnce(true);window.spin();cv::imshow("rgb", rgb);cv::imshow("depth", depth);cv::waitKey(1);cudaFree(hd_depth_ptr);cudaFree(cloud);}
}

运行结果如下

创建一个Patch管理GPU的2D内存
template <class T>
struct Patch
{
T *devPtr;
size_t pitch = 0;
size_t rows, cols;
Patch()
{
}
Patch(int rows, int cols) : rows(rows), cols(cols)
{
ck(cudaMallocPitch((void **)&devPtr, &pitch, cols * sizeof(T), rows));
ck(cudaMemset2D(devPtr, pitch, 0, sizeof(T) * cols, rows));
// std::cout << pitch << " " << sizeof(T) * cols << std::endl;
}
void creat(const int rows, const int cols)
{
this->rows = rows;
this->cols = cols;
ck(cudaMallocPitch((void **)&devPtr, &pitch, cols * sizeof(T), rows));
ck(cudaMemset2D(devPtr, pitch, 0, sizeof(T) * cols, rows));
}
//拷贝构造函数
__host__ __device__ Patch(const Patch &lth)
{
this->devPtr = lth.devPtr;
this->rows = lth.rows;
this->pitch = lth.pitch;
this->cols = lth.cols;
}

~Patch()
{
// cudaFree(devPtr);
}
void release()
{
cudaFree(devPtr);
}
__host__ void upload(const T *host_ptr_arg, size_t host_step_arg)
{
ck(cudaMemcpy2D(devPtr, pitch, host_ptr_arg, host_step_arg, cols * sizeof(T), rows, cudaMemcpyHostToDevice));
}
__host__ void download(const T *host_ptr_arg, size_t host_step_arg)
{
// if (host_step_arg == 0 || devPtr == nullptr || pitch == 0)
{
printf("%x,%d %x,%ld\n", host_ptr_arg, host_step_arg, devPtr, pitch);
}
ck(cudaMemcpy2D((void *)host_ptr_arg, host_step_arg, devPtr, pitch, sizeof(T) * cols, rows, cudaMemcpyDeviceToHost));
}
__device__ inline T &operator()(size_t rows, size_t cols)
{
// if (rows < pitch)
// return mat[x];
return *((devPtr + rows * pitch / sizeof(T) + cols));
}
__host__ void print()
{
printf("%ld %ld %ld\n", pitch, rows, cols);
}
};

TSDF类
tsdf class

修改后的主函数
int main()
{
DataSet<float> dt("/home/u20/dataset/paper/f3_long_office");
ck(cudaGetLastError());

cv::viz::Viz3d viz1("viz1"), viz2("viz2");
viz1.showWidget("Coordinate", cv::viz::WCoordinateSystem());

TSDF tsdf(make_float3(512, 512, 512), make_int2(640, 480));
tsdf.pintr = new Intr(make_float4(550, 550, 320, 240));
for (int i = 0; i < dt.pose.frames; i++)
{
cv::Mat rgb = cv::imread(dt.color_path[i]);
cv::Mat depth = cv::imread(dt.depth_path[i], cv::IMREAD_ANYDEPTH);

tsdf.addScan(depth, rgb);
std::shared_ptr<Mat> cpu_cloud2;
Mat cpu_color;
tsdf.exportCloud(cpu_cloud2, cpu_color);
viz1.showWidget("depthmode", cv::viz::WCloud(*cpu_cloud2, cpu_color));

Mat depth_color, cpu_cloud;
tsdf.depth2cam(depth, rgb, depth_color, cpu_color, cv::Affine3f::Identity());
cv::Affine3f viewpose = cv::Affine3f::Identity();
viz1.showWidget("depth", cv::viz::WCloud(depth_color, cpu_color), viewpose.translate(cv::Vec3f(4, 0, 0)));

// cv::imshow("rgb", rgb);
// cv::imshow("depth", depth);
// cv::waitKey(100);
viz1.spin();
}
}

最后效果如下

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版权声明：本文为CSDN博主「SkyTangLei」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/LiQuKanNaShiYu/article/details/125016578