Spaces:
Running on Zero
Running on Zero
File size: 10,196 Bytes
820f6f8 | 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 | from src.utils.typing_utils import *
import numpy as np
import torch
import torch.nn as nn
import scipy.ndimage
from skimage import measure
from einops import repeat
import torch.nn.functional as F
def generate_dense_grid_points(
bbox_min: np.ndarray, bbox_max: np.ndarray, octree_depth: int, indexing: str = "ij"
):
length = bbox_max - bbox_min
num_cells = np.exp2(octree_depth)
x = np.linspace(bbox_min[0], bbox_max[0], int(num_cells) + 1, dtype=np.float32)
y = np.linspace(bbox_min[1], bbox_max[1], int(num_cells) + 1, dtype=np.float32)
z = np.linspace(bbox_min[2], bbox_max[2], int(num_cells) + 1, dtype=np.float32)
[xs, ys, zs] = np.meshgrid(x, y, z, indexing=indexing)
xyz = np.stack((xs, ys, zs), axis=-1)
xyz = xyz.reshape(-1, 3)
grid_size = [int(num_cells) + 1, int(num_cells) + 1, int(num_cells) + 1]
return xyz, grid_size, length
def generate_dense_grid_points_gpu(
bbox_min: torch.Tensor,
bbox_max: torch.Tensor,
octree_depth: int,
indexing: str = "ij",
dtype: torch.dtype = torch.float16
):
length = bbox_max - bbox_min
num_cells = 2 ** octree_depth
device = bbox_min.device
x = torch.linspace(bbox_min[0], bbox_max[0], int(num_cells), dtype=dtype, device=device)
y = torch.linspace(bbox_min[1], bbox_max[1], int(num_cells), dtype=dtype, device=device)
z = torch.linspace(bbox_min[2], bbox_max[2], int(num_cells), dtype=dtype, device=device)
xs, ys, zs = torch.meshgrid(x, y, z, indexing=indexing)
xyz = torch.stack((xs, ys, zs), dim=-1)
xyz = xyz.view(-1, 3)
grid_size = [int(num_cells), int(num_cells), int(num_cells)]
return xyz, grid_size, length
def find_mesh_grid_coordinates_fast_gpu(
occupancy_grid,
n_limits=-1
):
core_grid = occupancy_grid[1:-1, 1:-1, 1:-1]
occupied = core_grid > 0
neighbors_unoccupied = (
(occupancy_grid[:-2, :-2, :-2] < 0)
| (occupancy_grid[:-2, :-2, 1:-1] < 0)
| (occupancy_grid[:-2, :-2, 2:] < 0) # x-1, y-1, z-1/0/1
| (occupancy_grid[:-2, 1:-1, :-2] < 0)
| (occupancy_grid[:-2, 1:-1, 1:-1] < 0)
| (occupancy_grid[:-2, 1:-1, 2:] < 0) # x-1, y0, z-1/0/1
| (occupancy_grid[:-2, 2:, :-2] < 0)
| (occupancy_grid[:-2, 2:, 1:-1] < 0)
| (occupancy_grid[:-2, 2:, 2:] < 0) # x-1, y+1, z-1/0/1
| (occupancy_grid[1:-1, :-2, :-2] < 0)
| (occupancy_grid[1:-1, :-2, 1:-1] < 0)
| (occupancy_grid[1:-1, :-2, 2:] < 0) # x0, y-1, z-1/0/1
| (occupancy_grid[1:-1, 1:-1, :-2] < 0)
| (occupancy_grid[1:-1, 1:-1, 2:] < 0) # x0, y0, z-1/1
| (occupancy_grid[1:-1, 2:, :-2] < 0)
| (occupancy_grid[1:-1, 2:, 1:-1] < 0)
| (occupancy_grid[1:-1, 2:, 2:] < 0) # x0, y+1, z-1/0/1
| (occupancy_grid[2:, :-2, :-2] < 0)
| (occupancy_grid[2:, :-2, 1:-1] < 0)
| (occupancy_grid[2:, :-2, 2:] < 0) # x+1, y-1, z-1/0/1
| (occupancy_grid[2:, 1:-1, :-2] < 0)
| (occupancy_grid[2:, 1:-1, 1:-1] < 0)
| (occupancy_grid[2:, 1:-1, 2:] < 0) # x+1, y0, z-1/0/1
| (occupancy_grid[2:, 2:, :-2] < 0)
| (occupancy_grid[2:, 2:, 1:-1] < 0)
| (occupancy_grid[2:, 2:, 2:] < 0) # x+1, y+1, z-1/0/1
)
core_mesh_coords = torch.nonzero(occupied & neighbors_unoccupied, as_tuple=False) + 1
if n_limits != -1 and core_mesh_coords.shape[0] > n_limits:
print(f"core mesh coords {core_mesh_coords.shape[0]} is too large, limited to {n_limits}")
ind = np.random.choice(core_mesh_coords.shape[0], n_limits, True)
core_mesh_coords = core_mesh_coords[ind]
return core_mesh_coords
def find_candidates_band(
occupancy_grid: torch.Tensor,
band_threshold: float,
n_limits: int = -1
) -> torch.Tensor:
"""
Returns the coordinates of all voxels in the occupancy_grid where |value| < band_threshold.
Args:
occupancy_grid (torch.Tensor): A 3D tensor of SDF values.
band_threshold (float): The threshold below which |SDF| must be to include the voxel.
n_limits (int): Maximum number of points to return (-1 for no limit)
Returns:
torch.Tensor: A 2D tensor of coordinates (N x 3) where each row is [x, y, z].
"""
core_grid = occupancy_grid[1:-1, 1:-1, 1:-1]
# logits to sdf
core_grid = torch.sigmoid(core_grid) * 2 - 1
# Create a boolean mask for all cells in the band
in_band = torch.abs(core_grid) < band_threshold
# Get coordinates of all voxels in the band
core_mesh_coords = torch.nonzero(in_band, as_tuple=False) + 1
if n_limits != -1 and core_mesh_coords.shape[0] > n_limits:
print(f"core mesh coords {core_mesh_coords.shape[0]} is too large, limited to {n_limits}")
ind = np.random.choice(core_mesh_coords.shape[0], n_limits, True)
core_mesh_coords = core_mesh_coords[ind]
return core_mesh_coords
def expand_edge_region_fast(edge_coords, grid_size, dtype):
expanded_tensor = torch.zeros(grid_size, grid_size, grid_size, device='cuda', dtype=dtype, requires_grad=False)
expanded_tensor[edge_coords[:, 0], edge_coords[:, 1], edge_coords[:, 2]] = 1
if grid_size < 512:
kernel_size = 5
pooled_tensor = torch.nn.functional.max_pool3d(expanded_tensor.unsqueeze(0).unsqueeze(0), kernel_size=kernel_size, stride=1, padding=2).squeeze()
else:
kernel_size = 3
pooled_tensor = torch.nn.functional.max_pool3d(expanded_tensor.unsqueeze(0).unsqueeze(0), kernel_size=kernel_size, stride=1, padding=1).squeeze()
expanded_coords_low_res = torch.nonzero(pooled_tensor, as_tuple=False).to(torch.int16)
expanded_coords_high_res = torch.stack([
torch.cat((expanded_coords_low_res[:, 0] * 2, expanded_coords_low_res[:, 0] * 2, expanded_coords_low_res[:, 0] * 2, expanded_coords_low_res[:, 0] * 2, expanded_coords_low_res[:, 0] * 2 + 1, expanded_coords_low_res[:, 0] * 2 + 1, expanded_coords_low_res[:, 0] * 2 + 1, expanded_coords_low_res[:, 0] * 2 + 1)),
torch.cat((expanded_coords_low_res[:, 1] * 2, expanded_coords_low_res[:, 1] * 2, expanded_coords_low_res[:, 1] * 2+1, expanded_coords_low_res[:, 1] * 2 + 1, expanded_coords_low_res[:, 1] * 2, expanded_coords_low_res[:, 1] * 2, expanded_coords_low_res[:, 1] * 2 + 1, expanded_coords_low_res[:, 1] * 2 + 1)),
torch.cat((expanded_coords_low_res[:, 2] * 2, expanded_coords_low_res[:, 2] * 2+1, expanded_coords_low_res[:, 2] * 2, expanded_coords_low_res[:, 2] * 2 + 1, expanded_coords_low_res[:, 2] * 2, expanded_coords_low_res[:, 2] * 2+1, expanded_coords_low_res[:, 2] * 2, expanded_coords_low_res[:, 2] * 2 + 1))
], dim=1)
return expanded_coords_high_res
def zoom_block(block, scale_factor, order=3):
block = block.astype(np.float32)
return scipy.ndimage.zoom(block, scale_factor, order=order)
def parallel_zoom(occupancy_grid, scale_factor):
result = torch.nn.functional.interpolate(occupancy_grid.unsqueeze(0).unsqueeze(0), scale_factor=scale_factor)
return result.squeeze(0).squeeze(0)
@torch.no_grad()
def hierarchical_extract_geometry(
geometric_func: Callable,
device: torch.device,
dtype: torch.dtype,
bounds: Union[Tuple[float], List[float], float] = (-1.25, -1.25, -1.25, 1.25, 1.25, 1.25),
dense_octree_depth: int = 8,
hierarchical_octree_depth: int = 9,
max_num_expanded_coords: int = 1e8,
verbose: bool = False,
):
"""
Args:
geometric_func:
device:
bounds:
dense_octree_depth:
hierarchical_octree_depth:
Returns:
"""
if isinstance(bounds, float):
bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds]
bbox_min = torch.tensor(bounds[0:3]).to(device)
bbox_max = torch.tensor(bounds[3:6]).to(device)
bbox_size = bbox_max - bbox_min
xyz_samples, grid_size, length = generate_dense_grid_points_gpu(
bbox_min=bbox_min,
bbox_max=bbox_max,
octree_depth=dense_octree_depth,
indexing="ij",
dtype=dtype
)
if verbose:
print(f'step 1 query num: {xyz_samples.shape[0]}')
grid_logits = geometric_func(xyz_samples.unsqueeze(0)).to(dtype).view(grid_size[0], grid_size[1], grid_size[2])
# print(f'step 1 grid_logits shape: {grid_logits.shape}')
for i in range(hierarchical_octree_depth - dense_octree_depth):
curr_octree_depth = dense_octree_depth + i + 1
# upsample
grid_size = 2**curr_octree_depth
normalize_offset = grid_size / 2
high_res_occupancy = parallel_zoom(grid_logits, 2).to(dtype)
band_threshold = 1.0
edge_coords = find_candidates_band(grid_logits, band_threshold)
expanded_coords = expand_edge_region_fast(edge_coords, grid_size=int(grid_size/2), dtype=dtype).to(dtype)
if verbose:
print(f'step {i+2} query num: {len(expanded_coords)}')
if max_num_expanded_coords > 0 and len(expanded_coords) > max_num_expanded_coords:
raise ValueError(f"expanded_coords is too large, {len(expanded_coords)} > {max_num_expanded_coords}")
expanded_coords_norm = (expanded_coords - normalize_offset) * (abs(bounds[0]) / normalize_offset)
all_logits = None
all_logits = geometric_func(expanded_coords_norm.unsqueeze(0)).to(dtype)
all_logits = torch.cat([expanded_coords_norm, all_logits[0]], dim=1)
# print("all logits shape = ", all_logits.shape)
indices = all_logits[..., :3]
indices = indices * (normalize_offset / abs(bounds[0])) + normalize_offset
indices = indices.type(torch.IntTensor)
values = all_logits[:, 3]
# breakpoint()
high_res_occupancy[indices[:, 0], indices[:, 1], indices[:, 2]] = values
grid_logits = high_res_occupancy
# torch.cuda.empty_cache()
if verbose:
print("final grids shape = ", grid_logits.shape)
vertices, faces, normals, _ = measure.marching_cubes(grid_logits.float().cpu().numpy(), 0, method="lewiner")
vertices = vertices / (2**hierarchical_octree_depth) * bbox_size.cpu().numpy() + bbox_min.cpu().numpy()
mesh_v_f = (vertices.astype(np.float32), np.ascontiguousarray(faces))
return mesh_v_f
|