Open3D 学习笔记

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  1. Introduction
    Open3D是什么？
    Open3D是支持快速处理3D对象的开源库（对比OpenCV对图像的支持）
    感谢作者：Qianyi Zhou http://qianyi.info/
    支持语言：Python, C++
    依赖：Numpy
    官方网站：http://www.open3d.org/
    文档与教程：http://www.open3d.org/docs/

  1. Installation

    • 从Pypi安装
      pip install –user open3d-python或 python3 -m pip install –user open3d-python
    • 从source安装
      $ git clone https://github.com/IntelVCL/Open3D
      Windows:
      　　Cmake GUI + VS2015(2017)
      　　对ALL和INSTALL项目分别生成解决方案
      Ubuntu:
      $ util/scripts/install-deps-ubuntu.sh
      $ mkdir build
      $ cd build
      $ cmake ../src -DCMAKE_INSTALL_PREFIX=open3d_install_directory
      $ make –j
      检验安装成功，Python中输入：
      import open3d
      help(open3d)
      For more information: http://www.open3d.org/docs/tutorial/Basic/python_interface.html

  2. Module Overview
    Open3D Module Overview
    其中Geometry包含以下三种表现形式的数据结构

    RGB-D Image
Pointcloud
Mesh

  3. Geometry Detail
    Image
    color_raw = read_image(“../../TestData/RGBD/color/00000.jpg”)
    depth_raw = read_image(“../../TestData/RGBD/depth/00000.png”)
    rgbd_image = create_rgbd_image_from_color_and_depth(color_raw, depth_raw)
    plt.subplot(1, 2, 1)
    plt.imshow(rgbd_image.color)
    plt.subplot(1, 2, 2)
    plt.imshow(rgbd_image.depth)
    plt.show()

    Pointcloud
    pcd = read_point_cloud(“../../TestData/fragment.ply“)
    # 支持：pcd ply xyz xyzrgb xyzn pts
    downpcd = voxel_down_sample(pcd, voxel_size = 0.05)
    estimate_normals(downpcd, search_param = KDTreeSearchParamHybrid(radius = 0.1, max_nn = 30))
    chair = vol.crop_point_cloud(pcd)
    vol = read_selection_polygon_volume(“../../TestData/Crop/cropped.json”)
    chair.paint_uniform_color([1, 0.706, 0])
    draw_geometries([chair])
    属性：
    np.asarray(pcd.points)
    pcd.normal
    pcd.colors
    其他方法：
    pcd.transform([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]])

    Mesh
    mesh = read_triangle_mesh(“../../TestData/knot.ply”)
    if (not mesh.has_vertex_normal): mesh.compute_vertex_normals()
    mesh.paint_uniform_color([r, g, b])
    draw_geometris([mesh])
    属性：
    mesh.triangles
    mesh.triangle_normal
    mesh1.triangles = Vector3iVector(np.asarray(mesh1.triangles)[:len(mesh1.triangles)//2, :])
    mesh.vertices
    mesh.vertice_colors
    mesh.vertice_normals

    Mesh - 自造元素
    mesh_sphere = create_mesh_sphere(radius = 1.0)
    mesh_sphere.compute_vertex_normals()
    mesh_sphere.paint_uniform_color([0.1, 0.1, 0.7])
    mesh_cylinder = create_mesh_cylinder(radius = 0.3, height = 4.0)
    mesh_cylinder.compute_vertex_normals()
    mesh_cylinder.paint_uniform_color([0.1, 0.9, 0.1])
    mesh_frame = create_mesh_coordinate_frame(size = 0.6, origin = [-2, -2, -2])

  4. Reconstruction Workflow
    Input: RGB-D Image

    • 特征提取和图像匹配
      • Matching pairs
      • Robust Pose Graph Optimization
      • Volumetric integration
    • 估算相机参数
      • Global Registration
      • Robust pose optimization
      • ICP Registration
      • Global non-rigid alignment
    • 重建点云 ply

    • 网格化 mesh

      可以由image直接生成点云
      pcd = create_point_cloud_from_rgbd_image(rgbd_image, PinholeCameraIntrinsic(PinholeCameraIntrinsicParameters.PrimeSenseDefault))

      也可由Odometry：读两幅RGBD图，计算刚体变换
      source_rgbd_image, target_rgbd_image
      option = OdometryOption()
      odo_init = np.identity(4)
      [success_hybrid_term, trans_hybrid_term, info] = compute_rgbd_odometry( source_rgbd_image, target_rgbd_image, pinhole_camera_intrinsic, odo_init, RGBDOdometryJacobianFromHybridTerm(), option)
      source_pcd_hybrid_term = create_point_cloud_from_rgbd_image(source_rgbd_image, pinhole_camera_intrinsic)
      source_pcd_hybrid_term.transform(trans_hybrid_term)
      draw_geometries([target_pcd, source_pcd_hybrid_term])

      ICP registration
      threshold = 0.02
      trans_init = np.asarray(4*4 matrix) # acquire from global registration
      reg_p2l = registration_icp(source, target, threshold, trans_init, transformationEstimationPointToPlane()) # TransformationEstimationPointToPoint
      draw_registration_result(source, target, reg_p2l.transformation)

  5. Advanced Part

---以上---