Spaces:
Running
on
Zero
Running
on
Zero
File size: 13,703 Bytes
11e6f7b |
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 |
# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
"""
The ray sampler is a module that takes in camera matrices and resolution and batches of rays.
Expects cam2world matrices that use the OpenCV camera coordinate system conventions.
"""
import torch
from pdb import set_trace as st
import random
HUGE_NUMBER = 1e10
TINY_NUMBER = 1e-6 # float32 only has 7 decimal digits precision
######################################################################################
# wrapper to simplify the use of nerfnet
######################################################################################
# https://github.com/Kai-46/nerfplusplus/blob/ebf2f3e75fd6c5dfc8c9d0b533800daaf17bd95f/ddp_model.py#L16
def depth2pts_outside(ray_o, ray_d, depth):
'''
ray_o, ray_d: [..., 3]
depth: [...]; inverse of distance to sphere origin
'''
# note: d1 becomes negative if this mid point is behind camera
d1 = -torch.sum(ray_d * ray_o, dim=-1) / torch.sum(ray_d * ray_d, dim=-1)
p_mid = ray_o + d1.unsqueeze(-1) * ray_d
p_mid_norm = torch.norm(p_mid, dim=-1)
ray_d_cos = 1. / torch.norm(ray_d, dim=-1)
d2 = torch.sqrt(1. - p_mid_norm * p_mid_norm) * ray_d_cos
p_sphere = ray_o + (d1 + d2).unsqueeze(-1) * ray_d
rot_axis = torch.cross(ray_o, p_sphere, dim=-1)
rot_axis = rot_axis / torch.norm(rot_axis, dim=-1, keepdim=True)
phi = torch.asin(p_mid_norm)
theta = torch.asin(p_mid_norm * depth) # depth is inside [0, 1]
rot_angle = (phi - theta).unsqueeze(-1) # [..., 1]
# now rotate p_sphere
# Rodrigues formula: https://en.wikipedia.org/wiki/Rodrigues%27_rotation_formula
p_sphere_new = p_sphere * torch.cos(rot_angle) + \
torch.cross(rot_axis, p_sphere, dim=-1) * torch.sin(rot_angle) + \
rot_axis * torch.sum(rot_axis*p_sphere, dim=-1, keepdim=True) * (1.-torch.cos(rot_angle))
p_sphere_new = p_sphere_new / torch.norm(
p_sphere_new, dim=-1, keepdim=True)
pts = torch.cat((p_sphere_new, depth.unsqueeze(-1)), dim=-1)
# now calculate conventional depth
depth_real = 1. / (depth + TINY_NUMBER) * torch.cos(theta) * ray_d_cos + d1
return pts, depth_real
class RaySampler(torch.nn.Module):
def __init__(self):
super().__init__()
self.ray_origins_h, self.ray_directions, self.depths, self.image_coords, self.rendering_options = None, None, None, None, None
def create_patch_uv(self,
patch_resolution,
resolution,
cam2world_matrix,
fg_bbox=None):
def sample_patch_uv(fg_bbox=None):
assert patch_resolution <= resolution
def sample_patch_range():
patch_reolution_start = random.randint(
0, resolution -
patch_resolution) # alias for randrange(start, stop+1)
# patch_reolution_end = patch_reolution_start + patch_resolution
return patch_reolution_start # , patch_reolution_end
def sample_patch_range_oversample_boundary(range_start=None,
range_end=None):
# left down corner undersampled
if range_start is None:
# range_start = patch_resolution // 2
range_start = patch_resolution
if range_end is None:
# range_end = resolution + patch_resolution // 2
range_end = resolution + patch_resolution
# oversample the boundary
patch_reolution_end = random.randint(
range_start,
range_end,
)
# clip range
if patch_reolution_end <= patch_resolution:
patch_reolution_end = patch_resolution
elif patch_reolution_end > resolution:
patch_reolution_end = resolution
# patch_reolution_end = patch_reolution_start + patch_resolution
return patch_reolution_end # , patch_reolution_end
# h_start = sample_patch_range()
# assert fg_bbox is not None
if fg_bbox is not None and random.random(
) > 0.125: # only train foreground. Has 0.1 prob to sample/train background.
# if fg_bbox is not None: # only train foreground. Has 0.1 prob to sample/train background.
# only return one UV here
top_min, left_min = fg_bbox[:, :2].min(dim=0,
keepdim=True)[0][0]
height_max, width_max = fg_bbox[:, 2:].max(dim=0,
keepdim=True)[0][0]
if top_min + patch_resolution < height_max:
h_end = sample_patch_range_oversample_boundary(
top_min + patch_resolution, height_max)
else:
h_end = max(
height_max.to(torch.uint8).item(), patch_resolution)
if left_min + patch_resolution < width_max:
w_end = sample_patch_range_oversample_boundary(
left_min + patch_resolution, width_max)
else:
w_end = max(
width_max.to(torch.uint8).item(), patch_resolution)
h_start = h_end - patch_resolution
w_start = w_end - patch_resolution
try:
assert h_start >= 0 and w_start >= 0
except:
st()
else:
h_end = sample_patch_range_oversample_boundary()
h_start = h_end - patch_resolution
w_end = sample_patch_range_oversample_boundary()
w_start = w_end - patch_resolution
assert h_start >= 0 and w_start >= 0
uv = torch.stack(
torch.meshgrid(
torch.arange(
start=h_start,
# end=h_start+patch_resolution,
end=h_end,
dtype=torch.float32,
device=cam2world_matrix.device),
torch.arange(
start=w_start,
# end=w_start + patch_resolution,
end=w_end,
dtype=torch.float32,
device=cam2world_matrix.device),
indexing='ij')) * (1. / resolution) + (0.5 / resolution)
uv = uv.flip(0).reshape(2, -1).transpose(1, 0) # ij -> xy
return uv, (h_start, w_start, patch_resolution, patch_resolution
) # top: int, left: int, height: int, width: int
all_uv = []
ray_bboxes = []
for _ in range(cam2world_matrix.shape[0]):
uv, bbox = sample_patch_uv(fg_bbox)
all_uv.append(uv)
ray_bboxes.append(bbox)
all_uv = torch.stack(all_uv, 0) # B patch_res**2 2
# ray_bboxes = torch.stack(ray_bboxes, 0) # B patch_res**2 2
return all_uv, ray_bboxes
def create_uv(self, resolution, cam2world_matrix):
uv = torch.stack(
torch.meshgrid(torch.arange(resolution,
dtype=torch.float32,
device=cam2world_matrix.device),
torch.arange(resolution,
dtype=torch.float32,
device=cam2world_matrix.device),
indexing='ij')) * (1. / resolution) + (0.5 /
resolution)
uv = uv.flip(0).reshape(2, -1).transpose(1, 0) # why
uv = uv.unsqueeze(0).repeat(cam2world_matrix.shape[0], 1, 1)
return uv
def forward(self, cam2world_matrix, intrinsics, resolution, fg_mask=None):
"""
Create batches of rays and return origins and directions.
cam2world_matrix: (N, 4, 4)
intrinsics: (N, 3, 3)
resolution: int
ray_origins: (N, M, 3)
ray_dirs: (N, M, 2)
"""
N, M = cam2world_matrix.shape[0], resolution**2
cam_locs_world = cam2world_matrix[:, :3, 3]
fx = intrinsics[:, 0, 0]
fy = intrinsics[:, 1, 1]
cx = intrinsics[:, 0, 2]
cy = intrinsics[:, 1, 2]
sk = intrinsics[:, 0, 1]
# uv = torch.stack(
# torch.meshgrid(torch.arange(resolution,
# dtype=torch.float32,
# device=cam2world_matrix.device),
# torch.arange(resolution,
# dtype=torch.float32,
# device=cam2world_matrix.device),
# indexing='ij')) * (1. / resolution) + (0.5 /
# resolution)
# uv = uv.flip(0).reshape(2, -1).transpose(1, 0) # why
# uv = uv.unsqueeze(0).repeat(cam2world_matrix.shape[0], 1, 1)
uv = self.create_uv(
resolution,
cam2world_matrix,
)
x_cam = uv[:, :, 0].view(N, -1)
y_cam = uv[:, :, 1].view(N, -1) # [0,1] range
z_cam = torch.ones((N, M), device=cam2world_matrix.device)
# basically torch.inverse(intrinsics)
x_lift = (x_cam - cx.unsqueeze(-1) + cy.unsqueeze(-1) *
sk.unsqueeze(-1) / fy.unsqueeze(-1) - sk.unsqueeze(-1) *
y_cam / fy.unsqueeze(-1)) / fx.unsqueeze(-1) * z_cam
y_lift = (y_cam - cy.unsqueeze(-1)) / fy.unsqueeze(-1) * z_cam
cam_rel_points = torch.stack(
(x_lift, y_lift, z_cam, torch.ones_like(z_cam)), dim=-1)
# st()
world_rel_points = torch.bmm(cam2world_matrix,
cam_rel_points.permute(0, 2, 1)).permute(
0, 2, 1)[:, :, :3]
ray_dirs = world_rel_points - cam_locs_world[:, None, :]
ray_dirs = torch.nn.functional.normalize(ray_dirs, dim=2)
ray_origins = cam_locs_world.unsqueeze(1).repeat(
1, ray_dirs.shape[1], 1)
return ray_origins, ray_dirs, None
class PatchRaySampler(RaySampler):
def forward(self,
cam2world_matrix,
intrinsics,
patch_resolution,
resolution,
fg_bbox=None):
"""
Create batches of rays and return origins and directions.
cam2world_matrix: (N, 4, 4)
intrinsics: (N, 3, 3)
resolution: int
ray_origins: (N, M, 3)
ray_dirs: (N, M, 2)
"""
N, M = cam2world_matrix.shape[0], patch_resolution**2
cam_locs_world = cam2world_matrix[:, :3, 3]
fx = intrinsics[:, 0, 0]
fy = intrinsics[:, 1, 1]
cx = intrinsics[:, 0, 2]
cy = intrinsics[:, 1, 2]
sk = intrinsics[:, 0, 1]
# uv = self.create_uv(resolution, cam2world_matrix)
# all_uv, ray_bboxes = self.create_patch_uv(
all_uv_list = []
ray_bboxes = []
for idx in range(N):
uv, bboxes = self.create_patch_uv(
patch_resolution, resolution, cam2world_matrix[idx:idx + 1],
fg_bbox[idx:idx + 1]
if fg_bbox is not None else None) # for debugging, hard coded
all_uv_list.append(
uv
# cam2world_matrix[idx:idx+1], )[0] # for debugging, hard coded
)
ray_bboxes.extend(bboxes)
all_uv = torch.cat(all_uv_list, 0)
# ray_bboxes = torch.cat(ray_bboxes_list, 0)
# all_uv, _ = self.create_patch_uv(
# patch_resolution, resolution,
# cam2world_matrix, fg_bbox) # for debugging, hard coded
# st()
x_cam = all_uv[:, :, 0].view(N, -1)
y_cam = all_uv[:, :, 1].view(N, -1) # [0,1] range
z_cam = torch.ones((N, M), device=cam2world_matrix.device)
# basically torch.inverse(intrinsics)
x_lift = (x_cam - cx.unsqueeze(-1) + cy.unsqueeze(-1) *
sk.unsqueeze(-1) / fy.unsqueeze(-1) - sk.unsqueeze(-1) *
y_cam / fy.unsqueeze(-1)) / fx.unsqueeze(-1) * z_cam
y_lift = (y_cam - cy.unsqueeze(-1)) / fy.unsqueeze(-1) * z_cam
cam_rel_points = torch.stack(
(x_lift, y_lift, z_cam, torch.ones_like(z_cam)), dim=-1)
world_rel_points = torch.bmm(cam2world_matrix,
cam_rel_points.permute(0, 2, 1)).permute(
0, 2, 1)[:, :, :3]
ray_dirs = world_rel_points - cam_locs_world[:, None, :]
ray_dirs = torch.nn.functional.normalize(ray_dirs, dim=2)
ray_origins = cam_locs_world.unsqueeze(1).repeat(
1, ray_dirs.shape[1], 1)
return ray_origins, ray_dirs, ray_bboxes
|