Pointcloud formats | commit New Tutorials Fit Textured Volume | commit Fit Neural Radiance Field | commit Small Updates Change io functions to use iopath | commit Read heterogenous nonlist PLY properties as arrays | commit Update the MeshRasterizer to automatically infer the camera type and set ...
import open3d as o3d print("->正在加载点云... ") pcd = o3d.io.read_point_cloud("test.pcd") print(pcd) print("->正在RANSAC平面分割...") distance_threshold = 0.2 # 内点到平面模型的最大距离 ransac_n = 3 # 用于拟合平面的采样点数 num_iterations = 1000 # 最大迭代次数 # 返回模型...
byte-compiling build\bdist.win-amd64\egg\pytorch3d\loss\mesh_normal_consistency.py to mesh_normal_consistency.cpython-37.pyc byte-compiling build\bdist.win-amd64\egg\pytorch3d\loss\point_mesh_distance.py to point_mesh_distance.cpython-37.pyc ...
chamfer_distance, the distance between the predicted (deformed) and target mesh, defined as the chamfer distance between the set of pointclouds resulting fromdifferentiably sampling pointsfrom their surfaces. However, solely minimizing the chamfer distance between the predicted and the target mesh will ...
由于COLMAP重建的mesh没有纹理贴图,这里使用pytorch3d.renderer.SoftSilhouetteShader()渲染mask。 lights=AmbientLights(device=device)#lights = PointLights(device=device, location=[[0.0, 0.0, -3.0]])rasterizer=MeshRasterizer(cameras=cameras,raster_settings=raster_settings)# Create a Phong renderer by composi...
Step # 2: Load the 3D mesh There are a couple of ways to represent 3D data, such aspoint clouds, meshes,orvoxels[6].In this tutorial we’ll focus on 3D meshesalthough the same procedure in PyTorch3D is applicable to point clouds too[7]. ...
append(loss_laplacian) # Plot mesh if i % plot_period == 0: plot_pointcloud(new_src_mesh, title="iter: %d" % i) # Optimization step loss.backward() optimizer.step() 经过几千次迭代,我们就可以从一个任意的曲面,重构出海豚的3d空间结构: 总结 本文只是带大家入门pytorch3d库,更多关于3d...
Pointclouds Voxel grids Signed distance functions and level sets Depth images (2.5D) kaolin提供了有效的PyTorch操作,可在3D表示形式之间进行转换。尽管网格,点云和体素网格仍然是最流行的3D表示形式,但高岭土广泛支持符号距离函数(SDF),正交深度图(ODM)和RGB-D图像。
_meshes(new_src_mesh,5000)# We compare the two sets of pointclouds by computing (a) the chamfer lossloss_chamfer,_=chamfer_distance(sample_trg,sample_src)# and (b) the edge length of the predicted meshloss_edge=mesh_edge_loss(new_src_mesh)# mesh normal consistencyloss_normal=mesh_...
_meshes(new_src_mesh,5000)# We compare the two sets of pointclouds by computing (a) the chamfer lossloss_chamfer,_=chamfer_distance(sample_trg,sample_src)# and (b) the edge length of the predicted meshloss_edge=mesh_edge_loss(new_src_mesh)# mesh normal consistencyloss_normal=mesh_...