-
Notifications
You must be signed in to change notification settings - Fork 1
/
sdf.py
430 lines (336 loc) · 19 KB
/
sdf.py
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
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
import numpy as np
import cv2
import os
from matplotlib.image import imread
from tqdm import tqdm
class SceneHelper:
def __init__(self, data_path, point_cloud_path, camera_extrinsics_path, max_resolution):
point_cloud = np.load(point_cloud_path)
camera_extrinsics = np.load(camera_extrinsics_path)
camera_intrinsics = torch.ones(1, device=device)*2378.98305085
camera_extrinsics = torch.from_numpy(np.array([np.hstack((cv2.Rodrigues(cam[:,:3])[0].ravel(), cam[:, 3].ravel())) for cam in camera_extrinsics])).float().to(device)
self.data_path = data_path
self.point_cloud = self.filter_point_cloud(point_cloud)
self.camera_extrinsics = camera_extrinsics
self.camera_intrinsics = camera_intrinsics
self.images, self.num_img = self.load_images()
self.min_bound, self.max_bound, self.resolution = (-2, -2, -2), (2, 2, 2), (max_resolution, max_resolution, max_resolution)
self.min_bound, self.max_bound, self.resolution = self.get_grid_resolution(max_resolution)
self.bounds = (self.min_bound, self.max_bound)
x = np.linspace(0, 1, 10)*((self.max_bound[0] - self.min_bound[0])) + self.min_bound[0]
y = np.linspace(0, 1, 10)*((self.max_bound[1] - self.min_bound[1])) + self.min_bound[1]
z = np.linspace(0, 1, 10)*((self.max_bound[2] - self.min_bound[2])) + self.min_bound[2]
grid = np.meshgrid(x, y, z, indexing='ij')
self.rectangular = np.stack(grid, axis=-1).reshape(-1, 3)
# self.rectangular = np.array([
# self.bounds[0],
# self.bounds[1],
# [self.bounds[0][0], self.bounds[0][1], self.bounds[1][2]],
# [self.bounds[0][0], self.bounds[1][1], self.bounds[1][2]],
# [self.bounds[1][0], self.bounds[0][1], self.bounds[1][2]],
# [self.bounds[0][0], self.bounds[1][1], self.bounds[0][2]],
# [self.bounds[1][0], self.bounds[1][1], self.bounds[0][2]],
# [self.bounds[1][0], self.bounds[0][1], self.bounds[0][2]]
# ])
def filter_point_cloud(self, verts):
verts = verts * 200
mean = np.mean(verts, axis=0)
temp = verts - mean
dist = np.sqrt(temp[:, 0] ** 2 + temp[:, 1] ** 2 + temp[:, 2] ** 2)
indx = np.where(dist < np.mean(dist) + 300)
verts = verts[indx]
return verts/200
def load_images(self):
img_list = []
images = []
image = None
with open("output/reconstructed_img.txt") as f:
img_list = f.readlines()
img_list = [l.strip() for l in img_list]
for i in range(len(img_list)):
image_path = img_list[i]
image = imread(os.path.join(self.data_path, image_path))
images.append(image)
return images, len(img_list)
def get_grid_resolution(self, max_resolution):
min_coords = np.min(self.point_cloud, axis=0)
max_coords = np.max(self.point_cloud, axis=0)
min_coords = (min_coords * 1.5).astype(int)
max_coords = (max_coords * 1.5).astype(int)
grid_size = max_coords - min_coords
size_box = np.max(grid_size) / max_resolution
grid_resolution = np.ceil(grid_size / size_box).astype(int)
grid_size = grid_resolution * size_box
return tuple(min_coords), tuple(max_coords), grid_resolution
def sample_batch(self, batch_size, img_index=0, sample_all=False):
image = self.images[img_index]
H, W = image.shape[:2]
if sample_all:
image_indices = (torch.zeros(W * H) + img_index).type(torch.long)
u, v = np.meshgrid(np.linspace(0, W - 1, W, dtype=int), np.linspace(0, H - 1, H, dtype=int))
u = torch.from_numpy(u.reshape(-1)).to(self.camera_intrinsics.device)
v = torch.from_numpy(v.reshape(-1)).to(self.camera_intrinsics.device)
else:
image_indices = (torch.zeros(batch_size) + img_index).type(torch.long) # Sample random images
u = torch.randint(W, (batch_size,), device=self.camera_intrinsics.device) # Sample random pixels
v = torch.randint(H, (batch_size,), device=self.camera_intrinsics.device)
focal = self.camera_intrinsics[0]
t = self.camera_extrinsics[img_index, :3]
r = self.camera_extrinsics[img_index, -3:]
# Creating the c2w matrix, Section 4.1 from the paper
phi_skew = torch.stack([torch.cat([torch.zeros(1, device=r.device), -r[2:3], r[1:2]]),
torch.cat([r[2:3], torch.zeros(1, device=r.device), -r[0:1]]),
torch.cat([-r[1:2], r[0:1], torch.zeros(1, device=r.device)])], dim=0)
alpha = r.norm() + 1e-15
R = torch.eye(3, device=r.device) + (torch.sin(alpha) / alpha) * phi_skew + (
(1 - torch.cos(alpha)) / alpha ** 2) * (phi_skew @ phi_skew)
c2w = torch.cat([R, t.unsqueeze(1)], dim=1)
c2w = torch.cat([c2w, torch.tensor([[0., 0., 0., 1.]], device=c2w.device)], dim=0)
rays_d_cam = torch.cat([((u.to(self.camera_intrinsics.device) - .5 * W) / focal).unsqueeze(-1),
(-(v.to(self.camera_intrinsics.device) - .5 * H) / focal).unsqueeze(-1),
- torch.ones_like(u).unsqueeze(-1)], dim=-1)
rays_d_world = torch.matmul(c2w[:3, :3].view(1, 3, 3), rays_d_cam.unsqueeze(2)).squeeze(2)
rays_o_world = c2w[:3, 3].view(1, 3).expand_as(rays_d_world).to(self.camera_intrinsics.device)
gt_px_values = torch.from_numpy(image[v.cpu(), u.cpu()]).to(self.camera_intrinsics.device)
return rays_o_world, F.normalize(rays_d_world, p=2, dim=1), gt_px_values
class GradientBasedSampler:
def __init__(self, num_samples=64, num_importance=32, perturb=True):
self.num_samples = num_samples
self.num_importance = num_importance
self.perturb = perturb
def ray_aabb_intersection(self, rays_o, rays_d, min_bound, max_bound):
inv_d = 1.0 / rays_d
t_min = (min_bound - rays_o) * inv_d
t_max = (max_bound - rays_o) * inv_d
t_near = torch.max(torch.min(t_min, t_max), dim=-1).values
t_far = torch.min(torch.max(t_min, t_max), dim=-1).values
t_near = torch.max(t_near, torch.tensor(0.0, device=t_near.device))
valid = t_far > t_near # Ensures t_far > t_near and t_near >= 0
return t_near, t_far, valid
def sample_uniform(self, rays_o, rays_d, t_near, t_far):
# Uniform sampling along the ray between t_near and t_far
t = torch.linspace(0, 1, self.num_samples, device=rays_o.device)
z = t_near[:, None] * (1 - t) + t_far[:, None] * t
if self.perturb:
mids = 0.5 * (z[:, 1:] + z[:, :-1])
upper = torch.cat([mids, z[:, -1:]], dim=-1)
lower = torch.cat([z[:, :1], mids], dim=-1)
t_rand = torch.rand_like(z)
z = lower + (upper - lower) * t_rand
pts = rays_o[:, None, :] + rays_d[:, None, :] * z[:, :, None]
return pts, z
def sample_importance(self, rays_o, rays_d, z_uniform, weights):
z_vals = self.sample_pdf(z_uniform, weights, self.num_importance, det=False)
pts = rays_o[:, None, :] + rays_d[:, None, :] * z_vals[:, :, None]
return pts, z_vals
# Helper function for importance sampling
def sample_pdf(self, bins, weights, N_samples, det=False):
weights = weights + 1e-5 # prevent nans
pdf = weights / torch.sum(weights, -1, keepdim=True)
cdf = torch.cumsum(pdf, -1)
cdf = torch.cat([torch.zeros_like(cdf[...,:1]), cdf], -1)
bins = torch.cat([bins[...,:1], bins], -1)
if det:
u = torch.linspace(0., 1., steps=N_samples).to(weights.device)
u = u.expand(list(cdf.shape[:-1]) + [N_samples])
else:
u = torch.rand(list(cdf.shape[:-1]) + [N_samples]).to(weights.device)
u = u.contiguous()
inds = torch.searchsorted(cdf, u, right=True)
below = torch.max(torch.zeros_like(inds-1), inds-1)
above = torch.min((cdf.shape[-1]-1) * torch.ones_like(inds), inds)
inds_g = torch.stack([below, above], -1)
matched_shape = [inds_g.shape[0], inds_g.shape[1], cdf.shape[-1]]
cdf_g = torch.gather(cdf.unsqueeze(1).expand(matched_shape), 2, inds_g)
bins_g = torch.gather(bins.unsqueeze(1).expand(matched_shape), 2, inds_g)
# bins_g = torch.gather(bins.unsqueeze(1).expand(inds_g.shape[0], inds_g.shape[1], bins.shape[-1]), 2, inds_g)
denom = (cdf_g[...,1] - cdf_g[...,0])
denom = torch.where(denom < 1e-5, torch.ones_like(denom), denom)
t = (u - cdf_g[...,0])/denom
samples = bins_g[...,0] + t * (bins_g[...,1]-bins_g[...,0])
return samples
def __call__(self, sdf_grid, rays_o, rays_d):
# Compute t_near and t_far from ray-box intersection
t_near, t_far, valid = self.ray_aabb_intersection(rays_o, rays_d, sdf_grid.min_bound, sdf_grid.max_bound)
# Only sample rays that intersect the grid
if not valid.any():
raise ValueError("No valid rays intersect the grid.")
rays_o = rays_o[valid]
rays_d = rays_d[valid]
t_near = t_near[valid]
t_far = t_far[valid]
# Initial uniform sampling based on t_near and t_far
pts_uniform, z_uniform = self.sample_uniform(rays_o, rays_d, t_near, t_far)
# Compute SDF and gradients
sdf_uniform = sdf_grid.get_sdf(pts_uniform.reshape(-1, 3)).reshape(pts_uniform.shape[:-1])
gradients = sdf_grid.get_sdf_gradient(pts_uniform.reshape(-1, 3)).reshape(pts_uniform.shape)
# Compute weights based on SDF gradient magnitude
gradient_mag = gradients.norm(dim=-1)
weights = F.softmax(gradient_mag, dim=-1)
# Importance sampling
pts_importance, z_importance = self.sample_importance(rays_o, rays_d, z_uniform, weights)
# Combine uniform and importance samples
pts = torch.cat([pts_uniform, pts_importance], dim=1)
z_vals = torch.cat([z_uniform, z_importance], dim=-1)
pts = pts_uniform
z_vals = z_uniform
# Sort samples by depth
z_vals, indices = torch.sort(z_vals, dim=-1)
pts = torch.gather(pts, 1, indices.unsqueeze(-1).expand_as(pts))
# Do not calculate grad of these variables
pts = pts.detach()
z_vals = z_vals.detach()
valid = valid.detach()
# Compute SDF for all points
sdf_values, sh_values = sdf_grid.get_sdf_sh(pts.reshape(-1, 3)) #.reshape(pts.shape[:-1])
sdf_values = sdf_values.reshape(pts.shape[:-1])
sh_values = sh_values.reshape(*pts.shape[:-1], 27)
return sdf_values, sh_values, pts, z_vals, valid
class SDFGrid(nn.Module):
def __init__(self, resolution, min_bound, max_bound, device):
super().__init__()
self.sampler = GradientBasedSampler(num_samples=160, num_importance=32)
self.resolution = resolution
self.min_bound = torch.tensor(min_bound).to(device)
self.max_bound = torch.tensor(max_bound).to(device)
self.grid = nn.Parameter(torch.ones(1, 27 + 1, *resolution) / 100)
self.alpha = nn.Parameter(torch.tensor(1.0))
self.beta = nn.Parameter(torch.tensor(0.0))
def get_sdf(self, points):
sdf = torch.zeros((points.shape[0]), device=points.device)
mask = self.test_points(points)
points = points[mask]
# Normalize points to [-1, 1] range
normalized_points = (points - self.min_bound) / (self.max_bound - self.min_bound) * 2 - 1
# Reshape for grid_sample
normalized_points = normalized_points.view(1, -1, 1, 1, 3)
# Trilinear interpolation
sdf_values = F.grid_sample(self.grid[:, 0:1, ...], normalized_points,
align_corners=True, mode='bilinear')
sdf[mask] = sdf_values.view(points.shape[:-1])
return sdf
def get_sdf_sh(self, points):
sh = torch.zeros((points.shape[0], 27), device=points.device)
sdf = torch.zeros((points.shape[0]), device=points.device)
mask = self.test_points(points)
old_point = points
points = points[mask]
# Normalize points to [-1, 1] range
normalized_points = (points - self.min_bound) / (self.max_bound - self.min_bound) * 2 - 1
# Reshape for grid_sample
normalized_points = normalized_points.view(1, -1, 1, 1, 3)
# Trilinear interpolation
sdf_values = F.grid_sample(self.grid[:, 0:1, ...], normalized_points,
align_corners=True, mode='bilinear')
sh_values = F.grid_sample(self.grid[:, 1:, ...], normalized_points,
align_corners=True, mode='bilinear')
sdf[mask] = sdf_values.view(points.shape[:-1])
sh[mask] = sh_values.view(points.shape[0], 27)
print(torch.sum(self.grid[:, :1, ...] != 0.01))
print(torch.sum(torch.isnan(points)), torch.sum(torch.isnan(old_point)))
print(torch.sum(torch.isnan(sdf)), torch.sum(torch.isnan(normalized_points)), torch.sum(torch.isnan(self.grid[:, :1, ...])), torch.sum(torch.isnan(self.grid[:, 1:, ...])))
return sdf, sh
def get_sdf_gradient(self, points):
points.requires_grad_(True)
sdf = self.get_sdf(points)
grad = torch.autograd.grad(sdf.sum(), points, create_graph=True)[0]
return grad
def eval_spherical_function(self, k, d):
x, y, z = d[..., 0:1], d[..., 1:2], d[..., 2:3]
# Modified from https://github.com/google/spherical-harmonics/blob/master/sh/spherical_harmonics.cc
return 0.282095 * k[..., 0] + \
- 0.488603 * y * k[..., 1] + 0.488603 * z * k[..., 2] - 0.488603 * x * k[..., 3] + \
(1.092548 * x * y * k[..., 4] - 1.092548 * y * z * k[..., 5] + 0.315392 * (2.0 * z * z - x * x - y * y) * k[
..., 6] + -1.092548 * x * z * k[..., 7] + 0.546274 * (x * x - y * y) * k[..., 8])
def compute_accumulated_transmittance(self, alphas):
accumulated_transmittance = torch.cumprod(alphas, 1)
return torch.cat((torch.ones((accumulated_transmittance.shape[0], 1), device=alphas.device),
accumulated_transmittance[:, :-1]), dim=-1)
def sdf_to_density(self, sdf):
# return torch.nn.functional.relu(sdf_values)
return 1 / (1 + torch.exp(-self.alpha * (sdf + self.beta)))
def test_camera(self, rays_o, rays_d):
try:
sdf_values, sh_values, pts, z_vals, valid = self.sampler(self, rays_o, rays_d)
return True
except:
return False
def test_points(self, x):
return (x[:, 0] >= self.min_bound[0]) & (x[:, 1] >= self.min_bound[1]) & (x[:, 2] >= self.min_bound[2]) & \
(x[:, 0] <= self.max_bound[0]) & (x[:, 1] <= self.max_bound[1]) & (x[:, 2] <= self.max_bound[2])
def forward(self, rays_o, rays_d):
sdf_values, sh_values, pts, z_vals, valid = self.sampler(self, rays_o, rays_d)
rays_o = rays_o[valid]
rays_d = rays_d[valid]
rays_d = rays_d.expand(z_vals.shape[1], rays_d.shape[0], 3).transpose(0, 1)
colors = self.eval_spherical_function(sh_values.reshape(-1, 3, 9), rays_d.reshape(-1, 3)).reshape(rays_d.shape)
sigma = self.sdf_to_density(sdf_values.reshape(-1)).reshape(sdf_values.shape)
delta = torch.cat((z_vals[:, 1:] - z_vals[:, :-1], torch.tensor([1e10], device=colors.device).expand(rays_o.shape[0], 1)), -1)
alpha = 1 - torch.exp(-sigma * delta) # [batch_size, nb_bins]
weights = self.compute_accumulated_transmittance(1 - alpha).unsqueeze(2) * alpha.unsqueeze(2)
c = (weights * colors).sum(dim=1) # Pixel values
weight_sum = weights.sum(-1).sum(-1) # Regularization for white background\
return c + 1 - weight_sum.unsqueeze(-1), pts, valid
if __name__ == "__main__":
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
nb_epochs = int(1e0)
batch_size = 1024
scene_helper = SceneHelper('ystad_kloster', "output/points_3d.npy", "output/cameras_extrinsic.npy", 250)
sdf_model = SDFGrid(scene_helper.resolution, scene_helper.min_bound, scene_helper.max_bound, device).to(device)
optimizer = torch.optim.Adam(sdf_model.parameters(), lr=1e-2)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[2, 4, 8], gamma=0.5)
training_dataset = torch.from_numpy(np.load('/home/coder/psrnet/nerf_datasets/training_data.pkl', allow_pickle=True))
data_loader = DataLoader(training_dataset, batch_size=2048, shuffle=True)
total_loss = []
for _ in range(nb_epochs):
training_loss = []
for batch in tqdm(data_loader):
ray_origins = batch[:, :3].to(device)
ray_directions = batch[:, 3:6].to(device)
ground_truth_px_values = batch[:, 6:].to(device)
regenerated_px_values, pts, valid = sdf_model(ray_origins, ray_directions)
loss = torch.nn.functional.mse_loss(ground_truth_px_values[valid], regenerated_px_values)
print("losss", loss.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()
training_loss.append(loss.item())
if(len(training_loss) % 170 == 0):
if len(total_loss) > 0:
print(training_loss)
total_loss.append(training_loss)
training_loss = []
scheduler.step()
# available_img = []
# for i in tqdm(range(scene_helper.num_img)):
# torch.cuda.empty_cache()
# rays_o, rays_d, gt_px_values = scene_helper.sample_batch(batch_size, img_index=i, sample_all=False)
# is_valid = sdf_model.test_camera(rays_o, rays_d)
# if is_valid:
# available_img.append(i)
# print(available_img)
# test_pts = np.array([[0, 0, 0]])
# total_loss = []
# for epoch in tqdm(range(nb_epochs)):
# training_loss = []
# for index, i in tqdm(enumerate(available_img)):
# torch.cuda.empty_cache()
# rays_o, rays_d, gt_px_values = scene_helper.sample_batch(batch_size, img_index=i, sample_all=False)
# pred_px_values, pts, valid = sdf_model(rays_o, rays_d)
# loss = torch.nn.functional.mse_loss(gt_px_values[valid].float()/255, pred_px_values.float())
# optimizer.zero_grad()
# loss.backward()
# optimizer.step()
# training_loss.append(loss.item())
# if index > 20 and index <= 25:
# print("SHAPE", pts.shape)
# test_pts = np.concatenate((test_pts, pts.reshape(-1, 3).detach().cpu().numpy()), axis=0)
# np.save('output/test_points.npy', np.concatenate((scene_helper.rectangular, test_pts)))
# if epoch > 0:
# print([0 if l1 - l2 > 0 else 1 for l1, l2 in zip(training_loss,total_loss[-1])])
# total_loss.append(training_loss)
# scheduler.step()