Comments (2)
Actually, the keypoints/anchorpoints can be easily obtained by the following code:
import numpy as np
import trimesh
from sklearn.cluster import KMeans
sample_num = 100000
point_num = 600
mesh = trimesh.load(input_mesh_path)
surface_points, _ = trimesh.sample.sample_surface(mesh, sample_num)
kmeans = KMeans(n_clusters=point_num, random_state=0).fit(surface_points)
key_points = kmeans.cluster_centers_
trimesh.Trimesh(vertices=key_points, faces=[]).export(out_file_off)
np.savez(out_file_path, kp=key_points)
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Thanks you very much for sharing!
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Related Issues (20)
- opt missing for training code HOT 2
- TARGET folder missing in provided preprocessed data HOT 2
- Multiple views support? HOT 2
- Target Sampling Distribution HOT 2
- Input mesh scaling HOT 1
- Missing data from the linked download page HOT 2
- trainning slow HOT 3
- failed:chamfer3D.cuda.0 想知道,cuda和cudnn的版本是什么呀,这个问题要怎么解决呢? HOT 3
- how to dress SMPL ? HOT 2
- 运行出错
- get_targets部分生成较慢 HOT 2
- 您好,想问一下quick demo生成的 文件 HOT 3
- 数据集处理中labels*缩放比例 HOT 4
- Some errors when running demo
- Large gap between training/eval performance
- gen_targets生成的数据可以直接分享一份吗?生成太慢了
- How to get reconstruction as shown in example images.
- UDF scaling during training HOT 1
- multiple GPUs support?
- issue with evaluation
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