1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
|
from plyfile import PlyData, PlyElement
from gaussian import Gaussian
import numpy as np
import os
def load_ply(path: str, max_sh_degree: int) -> Gaussian:
ply_data = PlyData.read(path)
# position
xyz = np.stack(
(
np.asarray(ply_data.elements[0]["x"]),
np.asarray(ply_data.elements[0]["y"]),
np.asarray(ply_data.elements[0]["z"]),
),
axis=1,
)
# opacity
def sigmoid(z):
return 1 / (1 + np.exp(-z))
opacities = np.asarray(ply_data.elements[0]["opacity"])[..., np.newaxis]
## 过激活函数
opacities = sigmoid(opacities)
# sh
features_dc = np.zeros((xyz.shape[0], 3, 1), dtype=np.float32)
features_dc[:, 0, 0] = np.asarray(ply_data.elements[0]["f_dc_0"])
features_dc[:, 1, 0] = np.asarray(ply_data.elements[0]["f_dc_1"])
features_dc[:, 2, 0] = np.asarray(ply_data.elements[0]["f_dc_2"])
extra_f_names = [
p.name for p in ply_data.elements[0].properties if p.name.startswith("f_rest_")
]
extra_f_names = sorted(extra_f_names, key=lambda x: int(x.split("_")[-1]))
assert len(extra_f_names) == 3 * (max_sh_degree + 1) ** 2 - 3
features_extra = np.zeros((xyz.shape[0], len(extra_f_names)), dtype=np.float32)
for idx, attr_name in enumerate(extra_f_names):
features_extra[:, idx] = np.asarray(ply_data.elements[0][attr_name])
# Reshape (P,F*SH_coeffs) to (P, F, SH_coeffs except DC)
features_extra = features_extra.reshape(
(features_extra.shape[0], 3, (max_sh_degree + 1) ** 2 - 1)
)
features_dc = np.transpose(features_dc, (0, 2, 1))
features_extra = np.transpose(features_extra, (0, 2, 1))
## 拼接得到完整的 sh
## sh = np.transpose(np.concatenate((features_dc, features_extra), axis=2), (0, 2, 1))
# scale
scale_names = [
p.name for p in ply_data.elements[0].properties if p.name.startswith("scale_")
]
scale_names = sorted(scale_names, key=lambda x: int(x.split("_")[-1]))
scales = np.zeros((xyz.shape[0], len(scale_names)), dtype=np.float32)
for idx, attr_name in enumerate(scale_names):
scales[:, idx] = np.asarray(ply_data.elements[0][attr_name])
## 过激活函数
scales = np.exp(scales)
# rotation
rot_names = [
p.name for p in ply_data.elements[0].properties if p.name.startswith("rot")
]
rot_names = sorted(rot_names, key=lambda x: int(x.split("_")[-1]))
rots = np.zeros((xyz.shape[0], len(rot_names)), dtype=np.float32)
for idx, attr_name in enumerate(rot_names):
rots[:, idx] = np.asarray(ply_data.elements[0][attr_name])
## 过激活函数
rot_length = np.linalg.norm(rots, axis=1)
rots = rots / np.expand_dims(rot_length, axis=1)
return Gaussian(xyz, scales, rots, features_dc, features_extra, opacities)
## return (num_gaussians, xyz, scales, rots, sh, opacities)
def save_ply(path: str, gaussian: Gaussian):
print(f"saving {path}")
os.makedirs(os.path.dirname(path), exist_ok=True)
def construct_list_of_attributes(gaussian):
l = ['x', 'y', 'z', 'nx', 'ny', 'nz']
# All channels except the 3 DC
for i in range(gaussian.features_dc.shape[1] * gaussian.features_dc.shape[2]):
l.append('f_dc_{}'.format(i))
for i in range(gaussian.features_rest.shape[1] * gaussian.features_rest.shape[2]):
l.append('f_rest_{}'.format(i))
l.append('opacity')
for i in range(gaussian.scales.shape[1]):
l.append('scale_{}'.format(i))
for i in range(gaussian.rotations.shape[1]):
l.append('rot_{}'.format(i))
return l
def inverse_sigmoid(x):
return np.log(x/(1-x))
xyz = gaussian.positions
normals = np.zeros_like(xyz)
f_dc = np.transpose(gaussian.features_dc, (0, 2, 1))
f_dc = np.reshape(f_dc, (f_dc.shape[0], f_dc.shape[1] * f_dc.shape[2]))
f_rest = np.transpose(gaussian.features_rest, (0, 2, 1))
f_rest = np.reshape(f_rest, (f_rest.shape[0], f_rest.shape[1] * f_rest.shape[2]))
opacities = inverse_sigmoid(gaussian.opacity)
scale = np.log(gaussian.scales)
rotation = gaussian.rotations
dtype_full = [(attribute, 'f4') for attribute in construct_list_of_attributes(gaussian)]
attributes = np.concatenate((xyz, normals, f_dc, f_rest, opacities, scale, rotation), axis=1)[:gaussian.num_gaussians]
elements = np.empty(attributes.shape[0], dtype=dtype_full)
elements[:] = list(map(tuple, attributes))
el = PlyElement.describe(elements, 'vertex')
PlyData([el]).write(path)
|
