-
Notifications
You must be signed in to change notification settings - Fork 84
/
renderer.py
170 lines (129 loc) · 6.32 KB
/
renderer.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
import torch
import torch.nn.functional as F
from utils import normal_vect, index_point_feature, build_color_volume
def depth2dist(z_vals, cos_angle):
# z_vals: [N_ray N_sample]
device = z_vals.device
dists = z_vals[..., 1:] - z_vals[..., :-1]
dists = torch.cat([dists, torch.Tensor([1e10]).to(device).expand(dists[..., :1].shape)], -1) # [N_rays, N_samples]
dists = dists * cos_angle.unsqueeze(-1)
return dists
def ndc2dist(ndc_pts):
dists = torch.norm(ndc_pts[:, 1:] - ndc_pts[:, :-1], dim=-1)
dists = torch.cat([dists, torch.Tensor([1e10]).to(ndc_pts.device).expand(dists[..., :1].shape)], -1) # [N_rays, N_samples]
return dists
def raw2alpha(sigma, dist, net_type):
alpha_softmax = F.softmax(sigma, 1)
alpha = 1. - torch.exp(-sigma)
T = torch.cumprod(torch.cat([torch.ones(alpha.shape[0], 1).to(alpha.device), 1. - alpha + 1e-10], -1), -1)[:, :-1]
weights = alpha * T # [N_rays, N_samples]
return alpha, weights, alpha_softmax
def batchify(fn, chunk):
"""Constructs a version of 'fn' that applies to smaller batches.
"""
if chunk is None:
return fn
def ret(inputs, alpha_only):
if alpha_only:
return torch.cat([fn.forward_alpha(inputs[i:i + chunk]) for i in range(0, inputs.shape[0], chunk)], 0)
else:
return torch.cat([fn(inputs[i:i + chunk]) for i in range(0, inputs.shape[0], chunk)], 0)
return ret
def run_network_mvs(pts, viewdirs, alpha_feat, fn, embed_fn, embeddirs_fn, netchunk=1024):
"""Prepares inputs and applies network 'fn'.
"""
if embed_fn is not None:
pts = embed_fn(pts)
if alpha_feat is not None:
pts = torch.cat((pts,alpha_feat), dim=-1)
if viewdirs is not None:
if viewdirs.dim()!=3:
viewdirs = viewdirs[:, None].expand(-1,pts.shape[1],-1)
if embeddirs_fn is not None:
viewdirs = embeddirs_fn(viewdirs)
pts = torch.cat([pts, viewdirs], -1)
alpha_only = viewdirs is None
outputs_flat = batchify(fn, netchunk)(pts, alpha_only)
outputs = torch.reshape(outputs_flat, list(pts.shape[:-1]) + [outputs_flat.shape[-1]])
return outputs
def raw2outputs(raw, z_vals, dists, white_bkgd=False, net_type='v2'):
"""Transforms model's predictions to semantically meaningful values.
Args:
raw: [num_rays, num_samples along ray, 4]. Prediction from model.
z_vals: [num_rays, num_samples along ray]. Integration time.
rays_d: [num_rays, 3]. Direction of each ray.
Returns:
rgb_map: [num_rays, 3]. Estimated RGB color of a ray.
disp_map: [num_rays]. Disparity map. Inverse of depth map.
acc_map: [num_rays]. Sum of weights along each ray.
weights: [num_rays, num_samples]. Weights assigned to each sampled color.
depth_map: [num_rays]. Estimated distance to object.
"""
device = z_vals.device
rgb = raw[..., :3] # [N_rays, N_samples, 3]
alpha, weights, alpha_softmax = raw2alpha(raw[..., 3], dists, net_type) # [N_rays, N_samples]
rgb_map = torch.sum(weights[..., None] * rgb, -2) # [N_rays, 3]
depth_map = torch.sum(weights * z_vals, -1)
disp_map = 1. / torch.max(1e-10 * torch.ones_like(depth_map, device=device), depth_map / torch.sum(weights, -1))
acc_map = torch.sum(weights, -1)
if white_bkgd:
rgb_map = rgb_map + (1. - acc_map[..., None])
return rgb_map, disp_map, acc_map, weights, depth_map, alpha
def gen_angle_feature(c2ws, rays_pts, rays_dir):
"""
Inputs:
c2ws: [1,v,4,4]
rays_pts: [N_rays, N_samples, 3]
rays_dir: [N_rays, 3]
Returns:
"""
N_rays, N_samples = rays_pts.shape[:2]
dirs = normal_vect(rays_pts.unsqueeze(2) - c2ws[:3, :3, 3][None, None]) # [N_rays, N_samples, v, 3]
angle = torch.sum(dirs[:, :, :3] * rays_dir.reshape(N_rays,1,1,3), dim=-1, keepdim=True).reshape(N_rays, N_samples, -1)
return angle
def gen_dir_feature(w2c_ref, rays_dir):
"""
Inputs:
c2ws: [1,v,4,4]
rays_pts: [N_rays, N_samples, 3]
rays_dir: [N_rays, 3]
Returns:
"""
dirs = rays_dir @ w2c_ref[:3,:3].t() # [N_rays, 3]
return dirs
def gen_pts_feats(imgs, volume_feature, rays_pts, pose_ref, rays_ndc, feat_dim, use_color_volume=False, net_type='v0'):
N_rays, N_samples = rays_pts.shape[:2]
if not use_color_volume:
input_feat = torch.empty((N_rays, N_samples, feat_dim), device=imgs.device, dtype=torch.float)
ray_feats = index_point_feature(volume_feature, rays_ndc) if torch.is_tensor(volume_feature) else volume_feature(rays_ndc)
input_feat[..., :8] = ray_feats
input_feat[..., 8:] = build_color_volume(rays_pts, pose_ref, imgs[:, :3], with_mask=True)
else:
input_feat = index_point_feature(volume_feature, rays_ndc) if torch.is_tensor(volume_feature) else volume_feature(rays_ndc)
return input_feat
def rendering(args, pose_ref, rays_pts, rays_ndc, depth_candidates, rays_o, rays_dir,
volume_feature=None, imgs=None, network_fn=None, network_fine=None, network_query_fn=None, white_bkgd=False, **kwargs):
# rays angle
cos_angle = torch.norm(rays_dir, dim=-1)
if 'v0' == args.net_type:
# using direction
angle = gen_dir_feature(pose_ref['w2cs'][0], rays_dir/cos_angle.unsqueeze(-1)) # view dir feature
else:
# using angle distance
angle = gen_angle_feature(pose_ref['c2ws'], rays_pts, rays_dir / cos_angle.unsqueeze(-1)) # angle feature
# rays_pts
input_feat = gen_pts_feats(imgs, volume_feature, rays_pts, pose_ref, rays_ndc, args.feat_dim, args.use_color_volume, args.net_type)
raw = network_query_fn(rays_ndc, angle, input_feat, network_fn)
dists = depth2dist(depth_candidates, cos_angle)
# dists = ndc2dist(rays_ndc)
rgb_map, disp_map, acc_map, weights, depth_map, alpha = raw2outputs(raw, depth_candidates, dists, white_bkgd,args.net_type)
ret = {}
return rgb_map, disp_map, acc_map, depth_map, alpha, ret
def render_density(network_fn, rays_pts, density_feature, network_query_fn, chunk=1024 * 5):
densities = []
device = density_feature.device
for i in range(0, rays_pts.shape[0], chunk):
input_feat = rays_pts[i:i + chunk].to(device)
density = network_query_fn(input_feat, None, density_feature[i:i + chunk], network_fn)
densities.append(density)
return torch.cat(densities)