pytorch3d.io.obj_io.save_obj() 和 pytorch3d.io.obj_io.load_obj_as_meshes() 提供集成的多纹理 obj 支持,允许用户读取和处理所有可用纹理; PyTorch3D 以前仅读取具有多个纹理的输入 obj 文件中的第一个纹理,这可能会导致不良的纹理采样和输出。 pytorch3d.utils.obj_utils 提供了可在 pytorch3d.ops 和 ...
打开看看里面有很多3D的模型: 这些obj的模型文件都可以通过pytorch3d来读取的。我们假设你已经下载好了上面的海豚3D文件,我们来read一下看看: """ using pytorch3d deform a source mesh to target mesh using 3D loss functions """ import os from pytorch3d.io import load_obj, save_obj from pytorch3d.st...
import os import torch from pytorch3d.io import load_obj, save_obj from pytorch3d.structures import Meshes from pytorch3d.utils import ico_sphere from pytorch3d.ops import sample_points_from_meshes from pytorch3d.loss import ( chamfer_distance, mesh_edge_loss, mesh_laplacian_smoothing, mesh_norm...
HardPhongShader, PointLights,)from pytorch3d.structures import Meshesfrom pytorch3d.io import load_obj# 1. 生成训练数据# 用PyTorch3D渲染一个球体作为示例数据verts, faces, aux = load_obj("sphere.obj") # 加载一个球体模型mesh = Meshes(verts=[verts], faces=[faces.verts_idx])# 设置相机R,...
Mesh loading - update toload_objto support loading textures as per face textures [following the approach from SoftRasterizer]. There is a new boolean argument called 'create_texture_atlasfor theload_obj` function to enable this |c9267ab ...
from pytorch3d.io import load_obj File "C:\ProgramData\Anaconda3\envs\pytorch3d\lib\site-packages\pytorch3d\io_init_.py", line 8, in from .obj_io import load_obj, load_objs_as_meshes, save_obj File "C:\ProgramData\Anaconda3\envs\pytorch3d\lib\site-packages\pytorch3d\io\obj_io.py...
cls = obj.find('name').text if cls not in classes or int(difficult)==1: continue cls_id = classes.index(cls) xmlbox = obj.find('bndbox') b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax'...
● Visual quality The pytorch implementation achieves better visual consistency with the input images compare to the original tensorflow version. ● Speed The training speed is on par with the original tensorflow implementation. For more information, seehere. ...
obj_ids = np.unique(mask) # first id is the background, so remove it obj_ids = obj_ids[1:] # split the color-encoded mask into a set # of binary masks masks = mask == obj_ids[:,None,None] # get bounding box coor...
pytorch随机采样操作SubsetRandomSampler（）