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78d2329 | 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 | from pathlib import Path
import numpy as np
import torch
import torch.nn.functional as F
from jaxtyping import Float
from plyfile import PlyData, PlyElement
from torch import Tensor
from optgs.model.types import Gaussians
from optgs.scene_trainer.gaussian_module import GaussiansModule
def construct_list_of_attributes(num_rest: int) -> list[str]:
attributes = ["x", "y", "z", "nx", "ny", "nz"]
for i in range(3):
attributes.append(f"f_dc_{i}")
for i in range(num_rest):
attributes.append(f"f_rest_{i}")
attributes.append("opacity")
for i in range(3):
attributes.append(f"scale_{i}")
for i in range(4):
attributes.append(f"rot_{i}")
return attributes
def export_ply(
# extrinsics: Float[Tensor, "4 4"],
means: Float[Tensor, "gaussian 3"],
scales: Float[Tensor, "gaussian 3"],
rotations: Float[Tensor, "gaussian 4"],
harmonics: Float[Tensor, "gaussian 3 d_sh"],
opacities: Float[Tensor, "gaussian"],
path: Path,
# align_to_view: bool = False, # whether to align world space to the view space (camera space) of the extrinsics
):
means = means.detach().cpu().numpy()
scales = scales.log().detach().cpu().numpy()
rotations = rotations.detach().cpu().numpy()
harmonics = harmonics.detach() # .cpu().numpy()
opacities = torch.logit(opacities[..., None]).detach().cpu().numpy()
num_rest = 3 * (harmonics.shape[-1] - 1)
dtype_full = [(attribute, "f4") for attribute in construct_list_of_attributes(num_rest)]
elements = np.empty(means.shape[0], dtype=dtype_full)
attributes = (
means,
np.zeros_like(means),
harmonics[..., 0].cpu().numpy(),
harmonics[..., 1:].flatten(start_dim=1).cpu().numpy(),
opacities,
scales,
rotations,
)
attributes = np.concatenate(attributes, axis=1)
elements[:] = list(map(tuple, attributes))
path.parent.mkdir(exist_ok=True, parents=True)
PlyData([PlyElement.describe(elements, "vertex")]).write(path)
def save_gaussian_ply(
gaussians: Gaussians | GaussiansModule,
save_path,
save_all_gaussians=True, # no trim
):
"""
Save Gaussians to a .ply file for visualization.
The saved object will have opacities and scales in the pre-activation space,
i.e., before applying the activation functions (sigmoid for opacity, exp for scales).
"""
if not save_all_gaussians:
raise NotImplementedError("Not implemented yet.")
if isinstance(gaussians, GaussiansModule):
# no batch dimension
means = gaussians.means # [H*W, 3]
rotations = gaussians.rotations # [H*W, 4] in xyzw
scales = gaussians.scales # [H*W, 3]
opacities = gaussians.opacities # [H*W]
harmonics = gaussians.harmonics # [H*W, 3, d_sh]
elif isinstance(gaussians, Gaussians):
assert gaussians.means.shape[0] == 1, "Batch size > 1 not supported for saving ply."
means = gaussians.means[0] # [H*W, 3]
rotations = F.normalize(gaussians.rotations_unnorm[0], dim=-1) # [H*W, 4] in xyzw
scales = gaussians.scales[0] # [H*W, 3]
opacities = gaussians.opacities[0] # [H*W]
harmonics = gaussians.harmonics[0] # [H*W, 3, d_sh]
# export_ply expects activated values (post-exp scales, post-sigmoid opacities)
# and applies inverse activation internally. If values are already deactivated,
# we must activate them first to avoid double inverse activation.
if not gaussians.stores_activated:
scales = torch.exp(scales)
opacities = torch.sigmoid(opacities)
else:
raise ValueError(f"Unknown type of gaussians: {type(gaussians)}")
# convert to wxyz for saving
rotations = rotations[:, [3, 0, 1, 2]] # [H*W, 4] in wxyz
# This fn invert activation of opacity and scales (for standard gaussian object, loaded by viewer)
export_ply(
means=means,
scales=scales,
rotations=rotations,
harmonics=harmonics, # [H*W, 3, d_sh]
opacities=opacities,
path=save_path,
)
def load_gaussians_ply(path, max_sh_degree=3) -> Gaussians:
""" Load Gaussians from a .ply file saved by export_ply().
The loaded object will have opacities and scales in the pre-activation space,
i.e., before applying the activation functions (sigmoid for opacity, expfor scales).
"""
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]))
#
if len(extra_f_names) == 0:
# loaded ply has no SH coefficients
# TODO: does this mean that features_dc probably encodes RGB which needs to be converted to SH0?
# all other features are zero
print("Loaded PLY has no SH coefficients, only DC features.")
features_extra = np.zeros((xyz.shape[0], 3, (max_sh_degree + 1) ** 2 - 1))
elif len(extra_f_names) == (3 * (max_sh_degree + 1) ** 2 - 3):
# loaded ply has full SH coefficients
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)
features_extra = features_extra.reshape((features_extra.shape[0], 3, (max_sh_degree + 1) ** 2 - 1))
else:
# not know how to handle
raise ValueError("Mismatch in number of SH coefficients.")
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])
# Create Gaussian object
means = torch.tensor(xyz, dtype=torch.float32) # [P, 3]
opacities = torch.tensor(opacities, dtype=torch.float32).squeeze(-1) # [P]
opacities = torch.sigmoid(opacities) # convert to post-activation space
harmonics = torch.zeros((xyz.shape[0], 3, (max_sh_degree + 1) ** 2), dtype=torch.float32) # [P, 3, d_sh]
harmonics[:, :, 0] = torch.tensor(features_dc[:, :, 0], dtype=torch.float32)
harmonics[:, :, 1:] = torch.tensor(features_extra, dtype=torch.float32)
scales = torch.tensor(scales, dtype=torch.float32)
scales = torch.exp(scales) # convert to post-activation space
quats = torch.tensor(rots, dtype=torch.float32) # in wxyz
quats = quats[:, [1, 2, 3, 0]] # convert to xyzw
quats = F.normalize(quats, dim=-1) # match 3DGS-LM get_rotation which normalizes before rendering
return Gaussians(
means=means.unsqueeze(0),
harmonics=harmonics.unsqueeze(0),
opacities=opacities.unsqueeze(0),
scales=scales.unsqueeze(0),
rotations=quats.unsqueeze(0),
rotations_unnorm=quats.unsqueeze(0),
)
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