File size: 10,527 Bytes
434b0b0 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 | # -*- coding: utf-8 -*-
# @Organization : Tongyi Lab, Alibaba
# @Author : Lingteng Qiu
# @Email : 220019047@link.cuhk.edu.cn
# @Time : 2025-10-15 16:25:39
# @Function : Gaussian Splatting Model Class
import math
import numpy as np
import torch
from plyfile import PlyData, PlyElement
from torch import Tensor
from core.models.rendering.utils.sh_utils import RGB2SH, SH2RGB
def inverse_sigmoid(x):
if isinstance(x, float):
x = torch.tensor(x).float()
return torch.log(x / (1 - x))
class GaussianModel:
"""
A class representing a differentiable model of 3D Gaussians for neural rendering.
The GaussianModel encapsulates per-point attributes such as 3D position (xyz),
opacity, rotation, scaling, and spherical harmonics coefficients or RGB appearance features.
It provides functionality for handling attribute activations, device management,
and efficient cloning of model instances. The model is compatible with torch operations and
serves as a core structure for Gaussian Splatting-based rendering and optimization pipelines.
"""
def setup_functions(self):
self.scaling_activation = torch.exp
self.scaling_inverse_activation = torch.log
self.opacity_activation = torch.sigmoid
self.inverse_opacity_activation = inverse_sigmoid
self.rotation_activation = torch.nn.functional.normalize
# rgb activation function
self.rgb_activation = torch.sigmoid
def __init__(self, xyz, opacity, rotation, scaling, shs, use_rgb=False) -> None:
"""
Initialize the GaussianModel instance.
Args:
xyz (Tensor): Tensor of shape [N, 3] representing the 3D positions of the Gaussians.
opacity (Tensor): Tensor of shape [N, C_opacity] representing the opacity values for each Gaussian.
rotation (Tensor): Tensor of shape [N, C_rotation] representing the rotations for each Gaussian.
scaling (Tensor): Tensor of shape [N, C_scaling] representing the scale factors for each Gaussian.
shs (Tensor): Tensor of shape [N, SH_Coeff, 3] containing the spherical harmonics coefficients or RGB features.
use_rgb (bool, optional): Whether to treat the appearance features as RGB. Default is False.
"""
self.setup_functions()
self.xyz: Tensor = xyz
self.opacity: Tensor = opacity
self.rotation: Tensor = rotation
self.scaling: Tensor = scaling
self.shs: Tensor = shs # [B, SH_Coeff, 3]
self.use_rgb = use_rgb # shs indicates rgb?
def construct_list_of_attributes(self):
"""
This function constructs and returns a list of attribute names representing the
features of each 3D Gaussian in the model. The attribute list includes coordinates,
normal vector components, spherical harmonics feature names for DC/rest,
opacity, scaling, and rotation parameter names.
"""
l = ["x", "y", "z", "nx", "ny", "nz"]
features_dc = self.shs[:, :1]
features_rest = self.shs[:, 1:]
for i in range(features_dc.shape[1] * features_dc.shape[2]):
l.append("f_dc_{}".format(i))
for i in range(features_rest.shape[1] * features_rest.shape[2]):
l.append("f_rest_{}".format(i))
l.append("opacity")
for i in range(self.scaling.shape[1]):
l.append("scale_{}".format(i))
for i in range(self.rotation.shape[1]):
l.append("rot_{}".format(i))
return l
def save_ply(self, path):
"""
Save the 3D Gaussian data and attributes to a PLY file.
Args:
path (str): The file path where the PLY file will be saved.
This method exports the 3D positions, normals, appearance features (either SH or RGB),
opacity, scaling, and rotation attributes of all Gaussians in the model into a structured
PLY format. The exported PLY will include all per-Gaussian attributes and can be used
for visualization or further processing in 3D graphics tools.
"""
xyz = self.xyz.detach().cpu().numpy()
normals = np.zeros_like(xyz)
if self.use_rgb:
shs = RGB2SH(self.shs)
else:
shs = self.shs
features_dc = shs[:, :1]
features_rest = shs[:, 1:]
f_dc = (
features_dc.float().detach().flatten(start_dim=1).contiguous().cpu().numpy()
)
f_rest = (
features_rest.float()
.detach()
.flatten(start_dim=1)
.contiguous()
.cpu()
.numpy()
)
opacities = (
inverse_sigmoid(torch.clamp(self.opacity, 1e-3, 1 - 1e-3))
.detach()
.cpu()
.numpy()
)
scale = np.log(self.scaling.detach().cpu().numpy())
rotation = self.rotation.detach().cpu().numpy()
dtype_full = [
(attribute, "f4") for attribute in self.construct_list_of_attributes()
]
elements = np.empty(xyz.shape[0], dtype=dtype_full)
attributes = np.concatenate(
(xyz, normals, f_dc, f_rest, opacities, scale, rotation), axis=1
)
elements[:] = list(map(tuple, attributes))
el = PlyElement.describe(elements, "vertex")
PlyData([el]).write(path)
def load_ply(self, path):
"""
Load the 3D Gaussian data and attributes from a PLY file.
Args:
path (str): The file path from which to load the PLY data.
This method reads the PLY file specified by 'path' and loads the 3D positions,
appearance features (SH or RGB), opacity, scaling, and rotation
attributes into the model. The method automatically infers the
spherical harmonics degree and reconstructs all tensors to be stored
in the current GaussianModel instance, updating its parameters accordingly.
"""
plydata = PlyData.read(path)
xyz = np.stack(
(
np.asarray(plydata.elements[0]["x"]),
np.asarray(plydata.elements[0]["y"]),
np.asarray(plydata.elements[0]["z"]),
),
axis=1,
)
opacities = np.asarray(plydata.elements[0]["opacity"])[..., np.newaxis]
features_dc = np.zeros((xyz.shape[0], 3, 1))
features_dc[:, 0, 0] = np.asarray(plydata.elements[0]["f_dc_0"])
features_dc[:, 1, 0] = np.asarray(plydata.elements[0]["f_dc_1"])
features_dc[:, 2, 0] = np.asarray(plydata.elements[0]["f_dc_2"])
extra_f_names = [
p.name
for p in plydata.elements[0].properties
if p.name.startswith("f_rest_")
]
extra_f_names = sorted(extra_f_names, key=lambda x: int(x.split("_")[-1]))
sh_degree = int(math.sqrt((len(extra_f_names) + 3) / 3)) - 1
print("load sh degree: ", sh_degree)
features_extra = np.zeros((xyz.shape[0], len(extra_f_names)))
for idx, attr_name in enumerate(extra_f_names):
features_extra[:, idx] = np.asarray(plydata.elements[0][attr_name])
# Reshape (P,F*SH_coeffs) to (P, F, SH_coeffs except DC)
# 0, 3, 8, 15
features_extra = features_extra.reshape(
(features_extra.shape[0], 3, (sh_degree + 1) ** 2 - 1)
)
scale_names = [
p.name
for p in plydata.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)))
for idx, attr_name in enumerate(scale_names):
scales[:, idx] = np.asarray(plydata.elements[0][attr_name])
rot_names = [
p.name for p in plydata.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)))
for idx, attr_name in enumerate(rot_names):
rots[:, idx] = np.asarray(plydata.elements[0][attr_name])
xyz = torch.from_numpy(xyz).to(self.xyz)
opacities = torch.from_numpy(opacities).to(self.opacity)
rotation = torch.from_numpy(rots).to(self.rotation)
scales = torch.from_numpy(scales).to(self.scaling)
features_dc = torch.from_numpy(features_dc).to(self.shs)
features_rest = torch.from_numpy(features_extra).to(self.shs)
shs = torch.cat([features_dc, features_rest], dim=2)
if self.use_rgb:
shs = SH2RGB(shs)
else:
shs = shs
self.xyz: Tensor = xyz
self.opacity: Tensor = self.opacity_activation(opacities)
self.rotation: Tensor = self.rotation_activation(rotation)
self.scaling: Tensor = self.scaling_activation(scales)
self.shs: Tensor = shs.permute(0, 2, 1)
self.active_sh_degree = sh_degree
def clone(self):
"""
Create a deep copy of the GaussianModel instance.
Returns:
GaussianModel: A new instance with identical parameter values.
"""
xyz = self.xyz.clone()
opacity = self.opacity.clone()
rotation = self.rotation.clone()
scaling = self.scaling.clone()
shs = self.shs.clone()
use_rgb = self.use_rgb
return GaussianModel(xyz, opacity, rotation, scaling, shs, use_rgb)
def CloneMaskGaussian(self, gs_deform_scale=0.005):
"""
Create a deep copy of the GaussianModel instance with only the xyz coordinates.
Args:
gs_deform_scale (float, optional): Deformation scale for Gaussian Splatting. Default 0.005.
Returns:
GaussianModel: A new instance with only the xyz coordinates.
"""
"""only containng xyz Gaussian!
"""
xyz = self.xyz.clone()
# Default settings.
# opacity = torch.ones_like(self.opacity)
opacity = self.opacity.clone().detach()
# Identity mapping
rotation = torch.ones_like(self.rotation.clone())
rotation[:, 1:] = 0
scaling = (
torch.ones_like(self.scaling) * gs_deform_scale
) # This is an empirically determined value
shs = torch.ones_like(self.shs)
use_rgb = self.use_rgb
return GaussianModel(xyz, opacity, rotation, scaling, shs, use_rgb)
|