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"""importosfrompytorch3d.ioimportload_obj,save_objfrompytorch3d.structuresimportM...
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...
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,...
2 changes: 1 addition & 1 deletion 2 pytorch3d/io/obj_io.py Original file line numberDiff line numberDiff line change @@ -750,7 +750,7 @@ def save_obj( if path_manager is None: path_manager = PathManager() save_texture = all([t is not None for t in [faces_uvs, verts_uvs...
Ability to save colors as 8bit (i.e. uint8) when writing data to PLY filescommit Internal Coarse rasterization code has been reorganizedhere,hereandhere Assets2 05 Aug 14:51 bottler v0.5.0 4046677 Compare Assets2 👍2metropolik and jaimezz reacted with thumbs up emoji🎉1metropolik react...
Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set —— PyTorch implementation This is an unofficial official pytorch implementation of the following paper: Y. Deng, J. Yang, S. Xu, D. Chen, Y. Jia, and X. Tong,Accurate 3D Face Reconstruction wit...
Namespace(accumulate=1, backend='nccl', batch_size=16, cfg='cfg/yolov3.cfg', data_cfg='data/coco.data', dist_url='tcp://127.0.0.1:9999', epochs=273, evolve=False, img_size=416, multi_scale=False, nosave=False, notest=False, num_workers=4, rank=0, resume=False, transfer=False...
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（）