Spaces:
Running
on
Zero
Running
on
Zero
File size: 8,151 Bytes
7f51798 |
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 |
import numpy as np
from pdb import set_trace as st
import torch
import torch.nn as nn
import torch.nn.functional as F
from diff_gaussian_rasterization import (
GaussianRasterizationSettings,
GaussianRasterizer,
)
import kiui
class GaussianRenderer:
def __init__(self, output_size, out_chans, rendering_kwargs, **kwargs):
# self.opt = opt
self.bg_color = torch.tensor([1, 1, 1], dtype=torch.float32, device="cuda")
# self.bg_color = torch.tensor([0,0,1], dtype=torch.float32, device="cuda")
self.output_size = output_size
self.out_chans = out_chans
self.rendering_kwargs = rendering_kwargs
# intrinsics
# self.tan_half_fov = np.tan(0.5 * np.deg2rad(self.opt.fovy))
# self.proj_matrix = torch.zeros(4, 4, dtype=torch.float32)
# self.proj_matrix[0, 0] = 1 / self.tan_half_fov
# self.proj_matrix[1, 1] = 1 / self.tan_half_fov
# self.proj_matrix[2, 2] = (opt.zfar + opt.znear) / (opt.zfar - opt.znear)
# self.proj_matrix[3, 2] = - (opt.zfar * opt.znear) / (opt.zfar - opt.znear)
# self.proj_matrix[2, 3] = 1
def render(self, gaussians, cam_view, cam_view_proj, cam_pos, tanfov, bg_color=None, scale_modifier=1):
# gaussians: [B, N, 14]
# cam_view, cam_view_proj: [B, V, 4, 4]
# cam_pos: [B, V, 3]
device = gaussians.device
B, V = cam_view.shape[:2]
# loop of loop...
images = []
alphas = []
depths = []
if bg_color is None:
bg_color = self.bg_color
for b in range(B):
# pos, opacity, scale, rotation, shs
means3D = gaussians[b, :, 0:3].contiguous().float()
opacity = gaussians[b, :, 3:4].contiguous().float()
scales = gaussians[b, :, 4:7].contiguous().float()
rotations = gaussians[b, :, 7:11].contiguous().float()
rgbs = gaussians[b, :, 11:].contiguous().float() # [N, 3]
for v in range(V):
# render novel views
view_matrix = cam_view[b, v].float()
view_proj_matrix = cam_view_proj[b, v].float()
campos = cam_pos[b, v].float()
raster_settings = GaussianRasterizationSettings(
image_height=self.output_size,
image_width=self.output_size,
tanfovx=tanfov,
tanfovy=tanfov,
bg=bg_color,
scale_modifier=scale_modifier,
viewmatrix=view_matrix,
projmatrix=view_proj_matrix,
sh_degree=0,
campos=campos,
prefiltered=False,
debug=False,
)
rasterizer = GaussianRasterizer(raster_settings=raster_settings)
# Rasterize visible Gaussians to image, obtain their radii (on screen).
rendered_image, radii, rendered_depth, rendered_alpha = rasterizer(
means3D=means3D,
means2D=torch.zeros_like(means3D, dtype=torch.float32, device=device),
shs=None,
colors_precomp=rgbs,
opacities=opacity,
scales=scales,
rotations=rotations,
cov3D_precomp=None,
)
rendered_image = rendered_image.clamp(0, 1)
images.append(rendered_image)
alphas.append(rendered_alpha)
depths.append(rendered_depth)
images = torch.stack(images, dim=0).view(B, V, 3, self.output_size, self.output_size)
alphas = torch.stack(alphas, dim=0).view(B, V, 1, self.output_size, self.output_size)
depths = torch.stack(depths, dim=0).view(B, V, 1, self.output_size, self.output_size)
# images = torch.stack(images, dim=0).view(B*V, 3, self.output_size, self.output_size)
# alphas = torch.stack(alphas, dim=0).view(B*V, 1, self.output_size, self.output_size)
# depths = torch.stack(depths, dim=0).view(B*V, 1, self.output_size, self.output_size)
return {
"image": images, # [B, V, 3, H, W]
"alpha": alphas, # [B, V, 1, H, W]
"depth": depths,
}
def save_ply(self, gaussians, path, compatible=True):
# gaussians: [B, N, 14]
# compatible: save pre-activated gaussians as in the original paper
assert gaussians.shape[0] == 1, 'only support batch size 1'
from plyfile import PlyData, PlyElement
means3D = gaussians[0, :, 0:3].contiguous().float()
opacity = gaussians[0, :, 3:4].contiguous().float()
scales = gaussians[0, :, 4:7].contiguous().float()
rotations = gaussians[0, :, 7:11].contiguous().float()
shs = gaussians[0, :, 11:].unsqueeze(1).contiguous().float() # [N, 1, 3]
# prune by opacity
mask = opacity.squeeze(-1) >= 0.005
means3D = means3D[mask]
opacity = opacity[mask]
scales = scales[mask]
rotations = rotations[mask]
shs = shs[mask]
# invert activation to make it compatible with the original ply format
if compatible:
opacity = kiui.op.inverse_sigmoid(opacity)
scales = torch.log(scales + 1e-8)
shs = (shs - 0.5) / 0.28209479177387814
xyzs = means3D.detach().cpu().numpy()
f_dc = shs.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy()
opacities = opacity.detach().cpu().numpy()
scales = scales.detach().cpu().numpy()
rotations = rotations.detach().cpu().numpy()
l = ['x', 'y', 'z']
# All channels except the 3 DC
for i in range(f_dc.shape[1]):
l.append('f_dc_{}'.format(i))
l.append('opacity')
for i in range(scales.shape[1]):
l.append('scale_{}'.format(i))
for i in range(rotations.shape[1]):
l.append('rot_{}'.format(i))
dtype_full = [(attribute, 'f4') for attribute in l]
elements = np.empty(xyzs.shape[0], dtype=dtype_full)
attributes = np.concatenate((xyzs, f_dc, opacities, scales, rotations), axis=1)
elements[:] = list(map(tuple, attributes))
el = PlyElement.describe(elements, 'vertex')
PlyData([el]).write(path)
def load_ply(self, path, compatible=True):
from plyfile import PlyData, PlyElement
plydata = PlyData.read(path)
xyz = np.stack((np.asarray(plydata.elements[0]["x"]),
np.asarray(plydata.elements[0]["y"]),
np.asarray(plydata.elements[0]["z"])), axis=1)
print("Number of points at loading : ", xyz.shape[0])
opacities = np.asarray(plydata.elements[0]["opacity"])[..., np.newaxis]
shs = np.zeros((xyz.shape[0], 3))
shs[:, 0] = np.asarray(plydata.elements[0]["f_dc_0"])
shs[:, 1] = np.asarray(plydata.elements[0]["f_dc_1"])
shs[:, 2] = np.asarray(plydata.elements[0]["f_dc_2"])
scale_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("scale_")]
scales = np.zeros((xyz.shape[0], len(scale_names)))
for idx, attr_name in enumerate(scale_names):
scales[:, idx] = np.asarray(plydata.elements[0][attr_name])
rot_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("rot_")]
rots = np.zeros((xyz.shape[0], len(rot_names)))
for idx, attr_name in enumerate(rot_names):
rots[:, idx] = np.asarray(plydata.elements[0][attr_name])
gaussians = np.concatenate([xyz, opacities, scales, rots, shs], axis=1)
gaussians = torch.from_numpy(gaussians).float() # cpu
if compatible:
gaussians[..., 3:4] = torch.sigmoid(gaussians[..., 3:4])
gaussians[..., 4:7] = torch.exp(gaussians[..., 4:7])
gaussians[..., 11:] = 0.28209479177387814 * gaussians[..., 11:] + 0.5
return gaussians |