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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 | from dataclasses import dataclass
from pathlib import Path
from typing import Literal, Optional
import numpy as np
import torch
import torch.nn.functional as F
from optgs.experimental.initializers_utils import knn, points_to_gaussians
from optgs.model.types import Gaussians
from optgs.scene_trainer.common.gaussian_adapter import build_covariance
from optgs.scene_trainer.initializer.initializer import NonlearnedInitializer, InitializerOutput, NonlearnedInitializerCfg
@dataclass
class InitializerPointcloudCfg(NonlearnedInitializerCfg):
name: Literal["pointcloud"]
path: Path # Directory containing <scene_id>.ply files
scaling_factor: float
init_opacity: float
sh_degree: int
filter_zero_rgb: bool
# 4x4 world transform applied to point cloud positions.
# Needed when the PLY is in a different coordinate system than the camera poses.
# For ScanNet++/NeRFstudio: the PLY is in COLMAP space while cameras are in
# NeRFstudio space. The transform is (x,y,z) -> (y,x,-z), i.e.:
# [[0,1,0,0],[1,0,0,0],[0,0,-1,0],[0,0,0,1]]
# Set to null to skip.
world_transform: Optional[list]
def get_gaussian_param_num(self):
sh_d = (self.sh_degree + 1) ** 2
return 3 + 4 + 3 * sh_d + 2 + 1
def get_sh_d(self):
return (self.sh_degree + 1) ** 2
class InitializerPointcloud(NonlearnedInitializer[InitializerPointcloudCfg]):
def __init__(self, cfg: InitializerPointcloudCfg) -> None:
super().__init__(cfg)
@staticmethod
def _load_ply(ply_path: Path) -> tuple[np.ndarray, np.ndarray]:
"""Load Open3D binary PLY file.
Returns:
xyz: [N, 3] float32 array of 3D positions
rgb: [N, 3] uint8 array of colors
"""
with open(ply_path, "rb") as f:
num_vertices = 0
while True:
line = f.readline().decode("ascii").strip()
if line.startswith("element vertex"):
num_vertices = int(line.split()[-1])
if line == "end_header":
break
dtype = np.dtype([
("x", "<f8"), ("y", "<f8"), ("z", "<f8"),
("r", "u1"), ("g", "u1"), ("b", "u1"),
])
data = np.frombuffer(f.read(num_vertices * dtype.itemsize), dtype=dtype)
xyz = np.stack([data["x"], data["y"], data["z"]], axis=1).astype(np.float32)
rgb = np.stack([data["r"], data["g"], data["b"]], axis=1)
return xyz, rgb
def forward(
self,
context,
visualization_dump: Optional[dict] = None,
device: Optional[torch.device] = None,
**kwargs
) -> InitializerOutput:
# Resolve PLY path
if "scene" in kwargs:
scene_name = kwargs["scene"]
assert len(scene_name) == 1, f"Only single scene initialization supported. {scene_name}"
scene_name = scene_name[0]
ply_path = self.cfg.path / f"{scene_name}.ply"
else:
raise ValueError("Scene name is required for pointcloud initializer.")
if not ply_path.exists():
raise ValueError(f"PLY file {ply_path} does not exist.")
print(f"Loading point cloud from {ply_path}")
# Load PLY
points_xyz, points_rgb = self._load_ply(ply_path)
print(f"Loaded {points_xyz.shape[0]} points.")
xyz = torch.from_numpy(points_xyz).float().to(device)
rgbs = torch.from_numpy(points_rgb / 255.0).float().to(device)
# Apply world transform to align point cloud with camera coordinate system
if self.cfg.world_transform is not None:
T = torch.tensor(self.cfg.world_transform, dtype=torch.float32, device=device)
# Transform: new_xyz = (T @ [xyz, 1])[:3]
xyz_h = torch.cat([xyz, torch.ones(xyz.shape[0], 1, device=device)], dim=-1) # [N, 4]
xyz = (T @ xyz_h.T)[:3].T # [N, 3]
# Filter zero-RGB points
if self.cfg.filter_zero_rgb:
valid_mask = rgbs.sum(dim=-1) > 0
xyz = xyz[valid_mask]
rgbs = rgbs[valid_mask]
# ── Step 1: subsampling augmentation ─────────────────────────────────────
min_sub = self.cfg.train_min_gaussians_subsample if self.training else self.cfg.eval_min_gaussians_subsample
max_sub = self.cfg.train_max_gaussians_subsample if self.training else self.cfg.eval_max_gaussians_subsample
if min_sub is not None or max_sub is not None:
target_count = self._sample_num_gaussians(xyz.shape[0], min_sub, max_sub)
if xyz.shape[0] > target_count:
indices = torch.randperm(xyz.shape[0], device=xyz.device)[:target_count]
xyz = xyz[indices]
rgbs = rgbs[indices]
# ── Step 2: subsample to fixed count (for DDP consistency) ────────────
fixed_num = self.cfg.train_fixed_gaussians_num if self.training else self.cfg.eval_fixed_gaussians_num
if fixed_num is not None and xyz.shape[0] > fixed_num:
indices = torch.randperm(xyz.shape[0], device=xyz.device)[:fixed_num]
xyz = xyz[indices]
rgbs = rgbs[indices]
# KNN → scales
dist2_avg = (knn(xyz, 4)[:, 1:] ** 2).mean(dim=-1) # [N,]
dist_avg = torch.sqrt(dist2_avg)
scales = dist_avg.unsqueeze(-1).repeat(1, 3) # [N, 3]
opacities = torch.full((xyz.shape[0],), self.cfg.init_opacity)
# Pad to fixed size for distributed training
if self.training and fixed_num is not None:
current_num = xyz.shape[0]
if current_num < fixed_num:
pad_size = fixed_num - current_num
xyz = F.pad(xyz, (0, 0, 0, pad_size), mode='constant', value=0.0)
rgbs = F.pad(rgbs, (0, 0, 0, pad_size), mode='constant', value=0.0)
scales = F.pad(scales, (0, 0, 0, pad_size), mode='constant', value=1e-10)
opacities = F.pad(opacities, (0, pad_size), mode='constant', value=1e-10)
points_dict = {
"xyz": xyz,
"rgb": rgbs,
"scales": scales * self.cfg.scaling_factor,
"opacities": opacities,
}
# Convert to Gaussian representation
gaussians_dict = points_to_gaussians(points_dict, sh_degree=self.cfg.sh_degree, device=device)
means = gaussians_dict["xyz"]
sh0 = gaussians_dict["sh0"]
shN = gaussians_dict["shN"]
harmonics = torch.cat([sh0, shN], dim=1) # [N, sh_d, 3]
harmonics = harmonics.permute(0, 2, 1) # [N, 3, sh_d]
rotations_unnorm = gaussians_dict["rotations_unnorm"]
opacities = torch.sigmoid(gaussians_dict["opacities_raw"])
scales = torch.exp(gaussians_dict["scales_raw"])
rotations = F.normalize(gaussians_dict["rotations_unnorm"], dim=-1)
covariances = build_covariance(scale=scales, rotation_xyzw=rotations)
gaussians = Gaussians(
means=means.unsqueeze(0),
covariances=covariances.unsqueeze(0),
harmonics=harmonics.unsqueeze(0),
opacities=opacities.unsqueeze(0),
scales=scales.unsqueeze(0),
rotations=rotations.unsqueeze(0),
rotations_unnorm=rotations_unnorm.unsqueeze(0),
)
return InitializerOutput(
gaussians=gaussians,
features=None,
depths=None,
)
@staticmethod
def _sample_num_gaussians(available: int, min_sub: int | float | None, max_sub: int | float | None) -> int:
"""Sample a target Gaussian count from the [min_sub, max_sub] range."""
if min_sub is None:
min_sub = max_sub
if max_sub is None:
max_sub = min_sub
if isinstance(min_sub, int):
target = torch.randint(min_sub, max_sub + 1, (1,)).item()
else: # float → ratio of available
ratio = torch.empty(1).uniform_(min_sub, max_sub).item()
target = int(available * ratio)
return min(target, available)
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