|
import torch |
|
import numpy as np |
|
from plyfile import PlyData, PlyElement |
|
from .general_utils import inverse_sigmoid, strip_symmetric, build_scaling_rotation |
|
|
|
|
|
class Gaussian: |
|
def __init__( |
|
self, |
|
aabb : list, |
|
sh_degree : int = 0, |
|
mininum_kernel_size : float = 0.0, |
|
scaling_bias : float = 0.01, |
|
opacity_bias : float = 0.1, |
|
scaling_activation : str = "exp", |
|
device='cuda' |
|
): |
|
self.init_params = { |
|
'aabb': aabb, |
|
'sh_degree': sh_degree, |
|
'mininum_kernel_size': mininum_kernel_size, |
|
'scaling_bias': scaling_bias, |
|
'opacity_bias': opacity_bias, |
|
'scaling_activation': scaling_activation, |
|
} |
|
|
|
self.sh_degree = sh_degree |
|
self.active_sh_degree = sh_degree |
|
self.mininum_kernel_size = mininum_kernel_size |
|
self.scaling_bias = scaling_bias |
|
self.opacity_bias = opacity_bias |
|
self.scaling_activation_type = scaling_activation |
|
self.device = device |
|
self.aabb = torch.tensor(aabb, dtype=torch.float32, device=device) |
|
self.setup_functions() |
|
|
|
self._xyz = None |
|
self._features_dc = None |
|
self._features_rest = None |
|
self._scaling = None |
|
self._rotation = None |
|
self._opacity = None |
|
|
|
def setup_functions(self): |
|
def build_covariance_from_scaling_rotation(scaling, scaling_modifier, rotation): |
|
L = build_scaling_rotation(scaling_modifier * scaling, rotation) |
|
actual_covariance = L @ L.transpose(1, 2) |
|
symm = strip_symmetric(actual_covariance) |
|
return symm |
|
|
|
if self.scaling_activation_type == "exp": |
|
self.scaling_activation = torch.exp |
|
self.inverse_scaling_activation = torch.log |
|
elif self.scaling_activation_type == "softplus": |
|
self.scaling_activation = torch.nn.functional.softplus |
|
self.inverse_scaling_activation = lambda x: x + torch.log(-torch.expm1(-x)) |
|
|
|
self.covariance_activation = build_covariance_from_scaling_rotation |
|
|
|
self.opacity_activation = torch.sigmoid |
|
self.inverse_opacity_activation = inverse_sigmoid |
|
|
|
self.rotation_activation = torch.nn.functional.normalize |
|
|
|
self.scale_bias = self.inverse_scaling_activation(torch.tensor(self.scaling_bias)).cuda() |
|
self.rots_bias = torch.zeros((4)).cuda() |
|
self.rots_bias[0] = 1 |
|
self.opacity_bias = self.inverse_opacity_activation(torch.tensor(self.opacity_bias)).cuda() |
|
|
|
@property |
|
def get_scaling(self): |
|
scales = self.scaling_activation(self._scaling + self.scale_bias) |
|
scales = torch.square(scales) + self.mininum_kernel_size ** 2 |
|
scales = torch.sqrt(scales) |
|
return scales |
|
|
|
@property |
|
def get_rotation(self): |
|
return self.rotation_activation(self._rotation + self.rots_bias[None, :]) |
|
|
|
@property |
|
def get_xyz(self): |
|
return self._xyz * self.aabb[None, 3:] + self.aabb[None, :3] |
|
|
|
@property |
|
def get_features(self): |
|
return torch.cat((self._features_dc, self._features_rest), dim=2) if self._features_rest is not None else self._features_dc |
|
|
|
@property |
|
def get_opacity(self): |
|
return self.opacity_activation(self._opacity + self.opacity_bias) |
|
|
|
def get_covariance(self, scaling_modifier = 1): |
|
return self.covariance_activation(self.get_scaling, scaling_modifier, self._rotation + self.rots_bias[None, :]) |
|
|
|
def from_scaling(self, scales): |
|
scales = torch.sqrt(torch.square(scales) - self.mininum_kernel_size ** 2) |
|
self._scaling = self.inverse_scaling_activation(scales) - self.scale_bias |
|
|
|
def from_rotation(self, rots): |
|
self._rotation = rots - self.rots_bias[None, :] |
|
|
|
def from_xyz(self, xyz): |
|
self._xyz = (xyz - self.aabb[None, :3]) / self.aabb[None, 3:] |
|
|
|
def from_features(self, features): |
|
self._features_dc = features |
|
|
|
def from_opacity(self, opacities): |
|
self._opacity = self.inverse_opacity_activation(opacities) - self.opacity_bias |
|
|
|
def construct_list_of_attributes(self): |
|
l = ['x', 'y', 'z', 'nx', 'ny', 'nz'] |
|
|
|
for i in range(self._features_dc.shape[1]*self._features_dc.shape[2]): |
|
l.append('f_dc_{}'.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): |
|
xyz = self.get_xyz.detach().cpu().numpy() |
|
normals = np.zeros_like(xyz) |
|
f_dc = self._features_dc.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy() |
|
opacities = inverse_sigmoid(self.get_opacity).detach().cpu().numpy() |
|
scale = torch.log(self.get_scaling).detach().cpu().numpy() |
|
rotation = (self._rotation + self.rots_bias[None, :]).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, opacities, scale, rotation), axis=1) |
|
elements[:] = list(map(tuple, attributes)) |
|
el = PlyElement.describe(elements, 'vertex') |
|
PlyData([el]).write(path) |
|
|
|
def load_ply(self, path): |
|
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"]) |
|
|
|
if self.sh_degree > 0: |
|
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])) |
|
assert len(extra_f_names)==3*(self.sh_degree + 1) ** 2 - 3 |
|
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]) |
|
|
|
features_extra = features_extra.reshape((features_extra.shape[0], 3, (self.max_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.tensor(xyz, dtype=torch.float, device=self.device) |
|
features_dc = torch.tensor(features_dc, dtype=torch.float, device=self.device).transpose(1, 2).contiguous() |
|
if self.sh_degree > 0: |
|
features_extra = torch.tensor(features_extra, dtype=torch.float, device=self.device).transpose(1, 2).contiguous() |
|
opacities = torch.sigmoid(torch.tensor(opacities, dtype=torch.float, device=self.device)) |
|
scales = torch.exp(torch.tensor(scales, dtype=torch.float, device=self.device)) |
|
rots = torch.tensor(rots, dtype=torch.float, device=self.device) |
|
|
|
|
|
self._xyz = (xyz - self.aabb[None, :3]) / self.aabb[None, 3:] |
|
self._features_dc = features_dc |
|
if self.sh_degree > 0: |
|
self._features_rest = features_extra |
|
else: |
|
self._features_rest = None |
|
self._opacity = self.inverse_opacity_activation(opacities) - self.opacity_bias |
|
self._scaling = self.inverse_scaling_activation(torch.sqrt(torch.square(scales) - self.mininum_kernel_size ** 2)) - self.scale_bias |
|
self._rotation = rots - self.rots_bias[None, :] |
|
|
