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import math
import numpy as np
import torch
from diff_gaussian_rasterization import (
GaussianRasterizationSettings,
GaussianRasterizer,
)
from sh_utils import eval_sh, SH2RGB, RGB2SH
from gaussian_model_4d import GaussianModel, BasicPointCloud
def getProjectionMatrix(znear, zfar, fovX, fovY):
tanHalfFovY = math.tan((fovY / 2))
tanHalfFovX = math.tan((fovX / 2))
P = torch.zeros(4, 4)
z_sign = 1.0
P[0, 0] = 1 / tanHalfFovX
P[1, 1] = 1 / tanHalfFovY
P[3, 2] = z_sign
P[2, 2] = z_sign * zfar / (zfar - znear)
P[2, 3] = -(zfar * znear) / (zfar - znear)
return P
class MiniCam:
def __init__(self, c2w, width, height, fovy, fovx, znear, zfar, time=0, gs_convention=True):
# c2w (pose) should be in NeRF convention.
self.image_width = width
self.image_height = height
self.FoVy = fovy
self.FoVx = fovx
self.znear = znear
self.zfar = zfar
w2c = np.linalg.inv(c2w)
if gs_convention:
# rectify...
w2c[1:3, :3] *= -1
w2c[:3, 3] *= -1
self.world_view_transform = torch.tensor(w2c).transpose(0, 1).cuda()
self.projection_matrix = (
getProjectionMatrix(
znear=self.znear, zfar=self.zfar, fovX=self.FoVx, fovY=self.FoVy
)
.transpose(0, 1)
.cuda()
)
self.full_proj_transform = self.world_view_transform @ self.projection_matrix
self.camera_center = -torch.tensor(c2w[:3, 3]).cuda()
self.time = time
class Renderer:
def __init__(self, opt, sh_degree=3, white_background=True, radius=1):
self.sh_degree = sh_degree
self.white_background = white_background
self.radius = radius
self.opt = opt
self.T = self.opt.batch_size
self.gaussians = GaussianModel(sh_degree, opt.deformation)
self.bg_color = torch.tensor(
[1, 1, 1] if white_background else [0, 0, 0],
dtype=torch.float32,
device="cuda",
)
self.means3D_deform_T = None
self.opacity_deform_T = None
self.scales_deform_T = None
self.rotations_deform_T = None
def initialize(self, input=None, num_pts=5000, radius=0.5):
# load checkpoint
if input is None:
# init from random point cloud
phis = np.random.random((num_pts,)) * 2 * np.pi
costheta = np.random.random((num_pts,)) * 2 - 1
thetas = np.arccos(costheta)
mu = np.random.random((num_pts,))
radius = radius * np.cbrt(mu)
x = radius * np.sin(thetas) * np.cos(phis)
y = radius * np.sin(thetas) * np.sin(phis)
z = radius * np.cos(thetas)
xyz = np.stack((x, y, z), axis=1)
# xyz = np.random.random((num_pts, 3)) * 2.6 - 1.3
shs = np.random.random((num_pts, 3)) / 255.0
pcd = BasicPointCloud(
points=xyz, colors=SH2RGB(shs), normals=np.zeros((num_pts, 3))
)
# self.gaussians.create_from_pcd(pcd, 10)
self.gaussians.create_from_pcd(pcd, 10, 1)
elif isinstance(input, BasicPointCloud):
# load from a provided pcd
self.gaussians.create_from_pcd(input, 1)
else:
# load from saved ply
self.gaussians.load_ply(input)
def prepare_render(
self,
):
means3D = self.gaussians.get_xyz
opacity = self.gaussians._opacity
scales = self.gaussians._scaling
rotations = self.gaussians._rotation
means3D_T = []
opacity_T = []
scales_T = []
rotations_T = []
time_T = []
for t in range(self.T):
time = torch.tensor(t).to(means3D.device).repeat(means3D.shape[0],1)
time = ((time.float() / self.T) - 0.5) * 2
means3D_T.append(means3D)
opacity_T.append(opacity)
scales_T.append(scales)
rotations_T.append(rotations)
time_T.append(time)
means3D_T = torch.cat(means3D_T)
opacity_T = torch.cat(opacity_T)
scales_T = torch.cat(scales_T)
rotations_T = torch.cat(rotations_T)
time_T = torch.cat(time_T)
means3D_deform_T, scales_deform_T, rotations_deform_T, opacity_deform_T = self.gaussians._deformation(means3D_T, scales_T,
rotations_T, opacity_T,
time_T) # time is not none
self.means3D_deform_T = means3D_deform_T.reshape([self.T, means3D_deform_T.shape[0]//self.T, -1])
self.opacity_deform_T = opacity_deform_T.reshape([self.T, means3D_deform_T.shape[0]//self.T, -1])
self.scales_deform_T = scales_deform_T.reshape([self.T, means3D_deform_T.shape[0]//self.T, -1])
self.rotations_deform_T = rotations_deform_T.reshape([self.T, means3D_deform_T.shape[0]//self.T, -1])
def prepare_render_4x(
self,
):
means3D = self.gaussians.get_xyz
opacity = self.gaussians._opacity
scales = self.gaussians._scaling
rotations = self.gaussians._rotation
means3D_T = []
opacity_T = []
scales_T = []
rotations_T = []
time_T = []
for t in range(self.T * 4):
tt = t / 4.
time = torch.tensor(tt).to(means3D.device).repeat(means3D.shape[0],1)
time = ((time.float() / self.T) - 0.5) * 2
means3D_T.append(means3D)
opacity_T.append(opacity)
scales_T.append(scales)
rotations_T.append(rotations)
time_T.append(time)
means3D_T = torch.cat(means3D_T)
opacity_T = torch.cat(opacity_T)
scales_T = torch.cat(scales_T)
rotations_T = torch.cat(rotations_T)
time_T = torch.cat(time_T)
means3D_deform_T, scales_deform_T, rotations_deform_T, opacity_deform_T = self.gaussians._deformation(means3D_T, scales_T,
rotations_T, opacity_T,
time_T) # time is not none
self.means3D_deform_T = means3D_deform_T.reshape([self.T *4, means3D_deform_T.shape[0]//self.T // 4, -1])
self.opacity_deform_T = opacity_deform_T.reshape([self.T*4, means3D_deform_T.shape[0]//self.T//4, -1])
self.scales_deform_T = scales_deform_T.reshape([self.T*4, means3D_deform_T.shape[0]//self.T//4, -1])
self.rotations_deform_T = rotations_deform_T.reshape([self.T*4, means3D_deform_T.shape[0]//self.T//4, -1])
def render(
self,
viewpoint_camera,
scaling_modifier=1.0,
bg_color=None,
override_color=None,
compute_cov3D_python=False,
convert_SHs_python=False,
):
# Create zero tensor. We will use it to make pytorch return gradients of the 2D (screen-space) means
screenspace_points = (
torch.zeros_like(
self.gaussians.get_xyz,
dtype=self.gaussians.get_xyz.dtype,
requires_grad=True,
device="cuda",
)
+ 0
)
try:
screenspace_points.retain_grad()
except:
pass
# Set up rasterization configuration
tanfovx = math.tan(viewpoint_camera.FoVx * 0.5)
tanfovy = math.tan(viewpoint_camera.FoVy * 0.5)
raster_settings = GaussianRasterizationSettings(
image_height=int(viewpoint_camera.image_height),
image_width=int(viewpoint_camera.image_width),
tanfovx=tanfovx,
tanfovy=tanfovy,
bg=self.bg_color if bg_color is None else bg_color,
scale_modifier=scaling_modifier,
viewmatrix=viewpoint_camera.world_view_transform,
projmatrix=viewpoint_camera.full_proj_transform,
sh_degree=self.gaussians.active_sh_degree,
campos=viewpoint_camera.camera_center,
prefiltered=False,
debug=False,
)
rasterizer = GaussianRasterizer(raster_settings=raster_settings)
means3D = self.gaussians.get_xyz
time = torch.tensor(viewpoint_camera.time).to(means3D.device).repeat(means3D.shape[0],1)
time = ((time.float() / self.T) - 0.5) * 2
means2D = screenspace_points
opacity = self.gaussians._opacity
# If precomputed 3d covariance is provided, use it. If not, then it will be computed from
# scaling / rotation by the rasterizer.
scales = None
rotations = None
cov3D_precomp = None
if compute_cov3D_python:
cov3D_precomp = self.gaussians.get_covariance(scaling_modifier)
else:
scales = self.gaussians._scaling
rotations = self.gaussians._rotation
means3D_deform, scales_deform, rotations_deform, opacity_deform = self.means3D_deform_T[viewpoint_camera.time], self.scales_deform_T[viewpoint_camera.time], self.rotations_deform_T[viewpoint_camera.time], self.opacity_deform_T[viewpoint_camera.time]
means3D_final = means3D + means3D_deform
rotations_final = rotations + rotations_deform
scales_final = scales + scales_deform
opacity_final = opacity + opacity_deform
scales_final = self.gaussians.scaling_activation(scales_final)
rotations_final = self.gaussians.rotation_activation(rotations_final)
opacity = self.gaussians.opacity_activation(opacity)
# If precomputed colors are provided, use them. Otherwise, if it is desired to precompute colors
# from SHs in Python, do it. If not, then SH -> RGB conversion will be done by rasterizer.
shs = None
colors_precomp = None
if colors_precomp is None:
if convert_SHs_python:
shs_view = self.gaussians.get_features.transpose(1, 2).view(
-1, 3, (self.gaussians.max_sh_degree + 1) ** 2
)
dir_pp = self.gaussians.get_xyz - viewpoint_camera.camera_center.repeat(
self.gaussians.get_features.shape[0], 1
)
dir_pp_normalized = dir_pp / dir_pp.norm(dim=1, keepdim=True)
sh2rgb = eval_sh(
self.gaussians.active_sh_degree, shs_view, dir_pp_normalized
)
colors_precomp = torch.clamp_min(sh2rgb + 0.5, 0.0)
else:
shs = self.gaussians.get_features
else:
colors_precomp = override_color
rendered_image, radii, rendered_depth, rendered_alpha = rasterizer(
means3D = means3D_final,
means2D = means2D,
shs = shs,
colors_precomp = colors_precomp,
opacities = opacity,
scales = scales_final,
rotations = rotations_final,
cov3D_precomp = cov3D_precomp)
rendered_image = rendered_image.clamp(0, 1)
# Those Gaussians that were frustum culled or had a radius of 0 were not visible.
# They will be excluded from value updates used in the splitting criteria.
return {
"image": rendered_image,
"depth": rendered_depth,
"alpha": rendered_alpha,
"viewspace_points": screenspace_points,
"visibility_filter": radii > 0,
"radii": radii,
}
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