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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from diff_gaussian_rasterization import (
GaussianRasterizationSettings,
GaussianRasterizer,
)
from .options import Options
import kiui
class GaussianRenderer:
def __init__(self, opt: Options):
self.opt = opt
self.bg_color = torch.tensor([1, 1, 1], dtype=torch.float32, device="cuda")
# 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,
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 = []
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.opt.output_size,
image_width=self.opt.output_size,
tanfovx=self.tan_half_fov,
tanfovy=self.tan_half_fov,
bg=self.bg_color if bg_color is None else 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)
images = torch.stack(images, dim=0).view(
B, V, 3, self.opt.output_size, self.opt.output_size
)
alphas = torch.stack(alphas, dim=0).view(
B, V, 1, self.opt.output_size, self.opt.output_size
)
return {
"image": images, # [B, V, 3, H, W]
"alpha": alphas, # [B, V, 1, H, W]
}
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
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