Spaces:
Build error
Build error
File size: 7,861 Bytes
efe5745 |
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 |
import numpy as np
import torch
from my3d import unproject
def subpixel_rays_from_img(H, W, K, c2w_pose, normalize_dir=True, f=8):
assert c2w_pose[3, 3] == 1.
H, W = H * f, W * f
n = H * W
ys, xs = np.meshgrid(range(H), range(W), indexing="ij")
xy_coords = np.stack([xs, ys], axis=-1).reshape(n, 2)
top_left = np.array([-0.5, -0.5]) + 1 / (2 * f)
xy_coords = top_left + xy_coords / f
ro = c2w_pose[:, -1]
pts = unproject(K, xy_coords, depth=1)
pts = pts @ c2w_pose.T
rd = pts - ro
rd = rd[:, :3]
if normalize_dir:
rd = rd / np.linalg.norm(rd, axis=-1, keepdims=True)
ro = np.tile(ro[:3], (n, 1))
return ro, rd
def rays_from_img(H, W, K, c2w_pose, normalize_dir=True):
assert c2w_pose[3, 3] == 1.
n = H * W
ys, xs = np.meshgrid(range(H), range(W), indexing="ij")
xy_coords = np.stack([xs, ys], axis=-1).reshape(n, 2)
ro = c2w_pose[:, -1]
pts = unproject(K, xy_coords, depth=1)
pts = pts @ c2w_pose.T
rd = pts - ro # equivalently can subtract [0,0,0,1] before pose transform
rd = rd[:, :3]
if normalize_dir:
rd = rd / np.linalg.norm(rd, axis=-1, keepdims=True)
ro = np.tile(ro[:3], (n, 1))
return ro, rd
def ray_box_intersect(ro, rd, aabb):
"""
Intersection of ray with axis-aligned bounding box
This routine works for arbitrary dimensions; commonly d = 2 or 3
only works for numpy, not torch (which has slightly diff api for min, max, and clone)
Args:
ro: [n, d] ray origin
rd: [n, d] ray direction (assumed to be already normalized;
if not still fine, meaning of t as time of flight holds true)
aabb: [d, 2] bbox bound on each dim
Return:
is_intersect: [n,] of bool, whether the particular ray intersects the bbox
t_min: [n,] ray entrance time
t_max: [n,] ray exit time
"""
n = ro.shape[0]
d = aabb.shape[0]
assert aabb.shape == (d, 2)
assert ro.shape == (n, d) and rd.shape == (n, d)
rd = rd.copy()
rd[rd == 0] = 1e-6 # avoid div overflow; logically safe to give it big t
ro = ro.reshape(n, d, 1)
rd = rd.reshape(n, d, 1)
ts = (aabb - ro) / rd # [n, d, 2]
t_min = ts.min(-1).max(-1) # [n,] last of entrance
t_max = ts.max(-1).min(-1) # [n,] first of exit
is_intersect = t_min < t_max
return is_intersect, t_min, t_max
def as_torch_tsrs(device, *args):
ret = []
for elem in args:
target_dtype = torch.float32 if np.issubdtype(elem.dtype, np.floating) else None
ret.append(
torch.as_tensor(elem, dtype=target_dtype, device=device)
)
return ret
def group_mask_filter(mask, *items):
return [elem[mask] for elem in items]
def mask_back_fill(tsr, N, inds, base_value=1.0):
shape = [N, *tsr.shape[1:]]
canvas = base_value * np.ones_like(tsr, shape=shape)
canvas[inds] = tsr
return canvas
def render_one_view(model, aabb, H, W, K, pose):
N = H * W
bs = max(W * 5, 4096) # render 5 rows; original batch size 4096, now 4000;
ro, rd = rays_from_img(H, W, K, pose)
ro, rd, t_min, t_max, intsct_inds = scene_box_filter(ro, rd, aabb)
n = len(ro)
# print(f"{n} vs {N}") # n can be smaller than N since some rays do not intsct aabb
# n = n // 1 # actual number of rays to render; only needed for fast debugging
dev = model.device
ro, rd, t_min, t_max = as_torch_tsrs(dev, ro, rd, t_min, t_max)
rgbs = torch.zeros(n, 3, device=dev)
depth = torch.zeros(n, 1, device=dev)
with torch.no_grad():
for i in range(int(np.ceil(n / bs))):
s = i * bs
e = min(n, s + bs)
_rgbs, _depth, _ = render_ray_bundle(
model, ro[s:e], rd[s:e], t_min[s:e], t_max[s:e]
)
rgbs[s:e] = _rgbs
depth[s:e] = _depth
rgbs, depth = rgbs.cpu().numpy(), depth.cpu().numpy()
base_color = 1.0 # empty region needs to be white
rgbs = mask_back_fill(rgbs, N, intsct_inds, base_color).reshape(H, W, 3)
depth = mask_back_fill(depth, N, intsct_inds, base_color).reshape(H, W)
return rgbs, depth
def scene_box_filter(ro, rd, aabb):
N = len(ro)
_, t_min, t_max = ray_box_intersect(ro, rd, aabb)
# do not render what's behind the ray origin
t_min, t_max = np.maximum(t_min, 0), np.maximum(t_max, 0)
# can test intersect logic by reducing the focal length
is_intsct = t_min < t_max
ro, rd, t_min, t_max = group_mask_filter(is_intsct, ro, rd, t_min, t_max)
intsct_inds = np.arange(N)[is_intsct]
return ro, rd, t_min, t_max, intsct_inds
def render_ray_bundle(model, ro, rd, t_min, t_max):
"""
The working shape is (k, n, 3) where k is num of samples per ray, n the ray batch size
During integration the reduction is applied on k
chain of filtering
starting with ro, rd (from cameras), and a scene bbox
- rays that do not intersect scene bbox; sample pts that fall outside the bbox
- samples that do not fall within alpha mask
- samples whose densities are very low; no need to compute colors on them
"""
num_samples, step_size = model.get_num_samples((t_max - t_min).max())
# print(num_samples)
n, k = len(ro), num_samples
# print(n,k)
ticks = step_size * torch.arange(k, device=ro.device)
ticks = ticks.view(k, 1, 1)
t_min = t_min.view(n, 1)
# t_min = t_min + step_size * torch.rand_like(t_min) # NOTE seems useless
t_max = t_max.view(n, 1)
dists = t_min + ticks # [n, 1], [k, 1, 1] -> [k, n, 1]
pts = ro + rd * dists # [n, 3], [n, 3], [k, n, 1] -> [k, n, 3]
mask = (ticks < (t_max - t_min)).squeeze(-1) # [k, 1, 1], [n, 1] -> [k, n, 1] -> [k, n]
smp_pts = pts[mask]
if model.alphaMask is not None:
alphas = model.alphaMask.sample_alpha(smp_pts)
alpha_mask = alphas > 0
mask[mask.clone()] = alpha_mask
smp_pts = pts[mask]
σ = torch.zeros(k, n, device=ro.device)
σ[mask] = model.compute_density_feats(smp_pts)
weights = volume_rend_weights(σ, step_size)
mask = weights > model.ray_march_weight_thres
smp_pts = pts[mask]
app_feats = model.compute_app_feats(smp_pts)
# viewdirs = rd.view(1, n, 3).expand(k, n, 3)[mask] # ray dirs for each point
# additional wild factors here as in nerf-w; wild factors are optimizable
c_dim = app_feats.shape[-1]
colors = torch.zeros(k, n, c_dim, device=ro.device)
colors[mask] = model.feats2color(app_feats)
weights = weights.view(k, n, 1) # can be used to compute other expected vals e.g. depth
bg_weight = 1. - weights.sum(dim=0) # [n, 1]
rgbs = (weights * colors).sum(dim=0) # [n, 3]
if model.blend_bg_texture:
uv = spherical_xyz_to_uv(rd)
bg_feats = model.compute_bg(uv)
bg_color = model.feats2color(bg_feats)
rgbs = rgbs + bg_weight * bg_color
else:
rgbs = rgbs + bg_weight * 1. # blend white bg color
# print(rgbs.shape)
# rgbs = rgbs.clamp(0, 1) # don't clamp since this is can be SD latent features
E_dists = (weights * dists).sum(dim=0)
bg_dist = 10. # blend bg distance; just don't make it too large
E_dists = E_dists + bg_weight * bg_dist
return rgbs, E_dists, weights.squeeze(-1)
def spherical_xyz_to_uv(xyz):
# xyz is Tensor of shape [N, 3], uv in [-1, 1]
x, y, z = xyz.t() # [N]
xy = (x ** 2 + y ** 2) ** 0.5
u = torch.atan2(xy, z) / torch.pi # [N]
v = torch.atan2(y, x) / (torch.pi * 2) + 0.5 # [N]
uv = torch.stack([u, v], -1) # [N, 2]
uv = uv * 2 - 1 # [0, 1] -> [-1, 1]
return uv
def volume_rend_weights(σ, dist):
α = 1 - torch.exp(-σ * dist)
T = torch.ones_like(α)
T[1:] = (1 - α).cumprod(dim=0)[:-1]
assert (T >= 0).all()
weights = α * T
return weights
|