我们使用open3D的姿势图后端,进行优化地图,具体查看:http://www.open3d.org/docs/latest/tutorial/Advanced/multiway_registration.html 实验结果
ICP (iterative closest point), 是对点云配准目前最常用的方法。其原理就是不断的对一个点云进行转换,并计算其中每个点与另外一个点云集的距离,将它转换成一个fitness score。然后不断地变换知道将这个总距离降到最低。一般来说icp都是经过全局配准之后运用的,也就是说两个点云要先被粗略地配准,然后icp会完成...
Using the Open3D tensor library, Open3D version 0.13 introduces a high-performance implementation of ICP, with support for multi-scale ICP. By iterating on different resolutions of the point cloud data in parallel, convergence of the models can be performed more quickly and efficiently with lower...
print('input') mesh = o3dtut.get_bunny_mesh() # fit to unit cube mesh.scale(1 / np.max(mesh.get_max_bound() - mesh.get_min_bound()), center=mesh.get_center()) o3d.visualization.draw_geometries([mesh]) print('voxelization') voxel_grid = o3d.geometry.VoxelGrid.create_from_triang...
:pyobject: multiscale_icp :lineno-match: Two options are given for the fine-grained registration. The ``color`` option is recommended since it uses color information to prevent drift. See [Park2017]_ Expand All @@ -33,9 +33,9 @@ Multiway registration .. literalinclude:: ../../.....
multiway_registration.py non_blocking_visualization.py pointcloud_outlier_removal.py rgbd_integration.py trajectory_io.py Basic file_io.py icp_registration.py kdtree.py mesh.py pointcloud.py python_binding.py rgbd_nyu.py rgbd_odometry.py rgbd_redwood.py rgbd_sun.py rgb...
[ ERROR] In open3d.cpu.pybind.t.pipelines.registration.multi_scale_icp : Invalid expression '(with default value)' pybind11_stubgen - [ ERROR] In open3d.cpu.pybind.t.pipelines.registration.robust_kernel.RobustKernel.__init__ : Invalid expression '<RobustKernelMethod.L2Loss: 0>' pybind11_...
# For Multi-Scale ICP (o3d.utility.DoubleVector): max_correspondence_distances = o3d.utility.DoubleVector([0.3, 0.14, 0.07]) (2)初始变换矩阵的初始化: 初始对齐通常通过全局配准算法获得。 # Initial alignment or source to target transform.init_source_to_target=np.asarray([[0.862,0.011,-0.507...
In this study, we introduce MeshMonk, an open-source resource for intensive 3D phenotyping on a large scale. Through dense-correspondence registration algorithms, like MeshMonk, we can advance our ability to integrate genomic and phenomic data to explore variation in complex morphological traits and ...
It can be utilized to encode, decode and fuse biplanar multi-scale features. First, the encoders, which are down-sampling operations with dense connections, extract features and expand of 2D-3D dimensions of each imaging view separately. Second, the decoders, which are up- sampling operations ...