File size: 5,682 Bytes
355b5d6 |
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 |
'''
MIT License
Copyright (c) 2019 Shunsuke Saito, Zeng Huang, and Ryota Natsume
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
'''
import numpy as np
def create_grid(resX, resY, resZ, b_min=np.array([-1, -1, -1]), b_max=np.array([1, 1, 1]), transform=None):
'''
Create a dense grid of given resolution and bounding box
:param resX: resolution along X axis
:param resY: resolution along Y axis
:param resZ: resolution along Z axis
:param b_min: vec3 (x_min, y_min, z_min) bounding box corner
:param b_max: vec3 (x_max, y_max, z_max) bounding box corner
:return: [3, resX, resY, resZ] coordinates of the grid, and transform matrix from mesh index
'''
coords = np.mgrid[:resX, :resY, :resZ]
coords = coords.reshape(3, -1)
coords_matrix = np.eye(4)
length = b_max - b_min
coords_matrix[0, 0] = length[0] / resX
coords_matrix[1, 1] = length[1] / resY
coords_matrix[2, 2] = length[2] / resZ
coords_matrix[0:3, 3] = b_min
coords = np.matmul(coords_matrix[:3, :3], coords) + coords_matrix[:3, 3:4]
if transform is not None:
coords = np.matmul(transform[:3, :3], coords) + transform[:3, 3:4]
coords_matrix = np.matmul(transform, coords_matrix)
coords = coords.reshape(3, resX, resY, resZ)
return coords, coords_matrix
def batch_eval(points, eval_func, num_samples=512 * 512 * 512):
num_pts = points.shape[1]
sdf = np.zeros(num_pts)
num_batches = num_pts // num_samples
for i in range(num_batches):
sdf[i * num_samples:i * num_samples + num_samples] = eval_func(
points[:, i * num_samples:i * num_samples + num_samples])
if num_pts % num_samples:
sdf[num_batches * num_samples:] = eval_func(points[:, num_batches * num_samples:])
return sdf
def batch_eval_tensor(points, eval_func, num_samples=512 * 512 * 512):
num_pts = points.size(1)
num_batches = num_pts // num_samples
vals = []
for i in range(num_batches):
vals.append(eval_func(points[:, i * num_samples:i * num_samples + num_samples]))
if num_pts % num_samples:
vals.append(eval_func(points[:, num_batches * num_samples:]))
return np.concatenate(vals,0)
def eval_grid(coords, eval_func, num_samples=512 * 512 * 512):
resolution = coords.shape[1:4]
coords = coords.reshape([3, -1])
sdf = batch_eval(coords, eval_func, num_samples=num_samples)
return sdf.reshape(resolution)
import time
def eval_grid_octree(coords, eval_func,
init_resolution=64, threshold=0.05,
num_samples=512 * 512 * 512):
resolution = coords.shape[1:4]
sdf = np.zeros(resolution)
notprocessed = np.zeros(resolution, dtype=np.bool)
notprocessed[:-1,:-1,:-1] = True
grid_mask = np.zeros(resolution, dtype=np.bool)
reso = resolution[0] // init_resolution
while reso > 0:
# subdivide the grid
grid_mask[0:resolution[0]:reso, 0:resolution[1]:reso, 0:resolution[2]:reso] = True
# test samples in this iteration
test_mask = np.logical_and(grid_mask, notprocessed)
# print('step size:', reso, 'test sample size:', test_mask.sum())
points = coords[:, test_mask]
sdf[test_mask] = batch_eval(points, eval_func, num_samples=num_samples)
notprocessed[test_mask] = False
# do interpolation
if reso <= 1:
break
x_grid = np.arange(0, resolution[0], reso)
y_grid = np.arange(0, resolution[1], reso)
z_grid = np.arange(0, resolution[2], reso)
v = sdf[tuple(np.meshgrid(x_grid, y_grid, z_grid, indexing='ij'))]
v0 = v[:-1,:-1,:-1]
v1 = v[:-1,:-1,1:]
v2 = v[:-1,1:,:-1]
v3 = v[:-1,1:,1:]
v4 = v[1:,:-1,:-1]
v5 = v[1:,:-1,1:]
v6 = v[1:,1:,:-1]
v7 = v[1:,1:,1:]
x_grid = x_grid[:-1] + reso//2
y_grid = y_grid[:-1] + reso//2
z_grid = z_grid[:-1] + reso//2
nonprocessed_grid = notprocessed[tuple(np.meshgrid(x_grid, y_grid, z_grid, indexing='ij'))]
v = np.stack([v0,v1,v2,v3,v4,v5,v6,v7], 0)
v_min = v.min(0)
v_max = v.max(0)
v = 0.5*(v_min+v_max)
skip_grid = np.logical_and(((v_max - v_min) < threshold), nonprocessed_grid)
n_x = resolution[0] // reso
n_y = resolution[1] // reso
n_z = resolution[2] // reso
xs, ys, zs = np.where(skip_grid)
for x, y, z in zip(xs*reso, ys*reso, zs*reso):
sdf[x:(x+reso+1), y:(y+reso+1), z:(z+reso+1)] = v[x//reso,y//reso,z//reso]
notprocessed[x:(x+reso+1), y:(y+reso+1), z:(z+reso+1)] = False
reso //= 2
return sdf.reshape(resolution)
|