mirror of
https://github.com/graphdeco-inria/gaussian-splatting
synced 2024-11-22 16:28:32 +00:00
89 lines
3.3 KiB
Python
89 lines
3.3 KiB
Python
|
import torch
|
||
|
|
import math
|
||
|
|
from diff_gaussian_rasterization import GaussianRasterizationSettings, GaussianRasterizer
|
||
|
|
from scene.gaussian_model import GaussianModel
|
||
|
|
from utils.sh_utils import eval_sh
|
||
|
|
|
||
|
|
def render(viewpoint_camera, pc : GaussianModel, pipe, bg_color : torch.Tensor, scaling_modifier = 1.0, override_color = None):
|
||
|
|
"""
|
||
|
|
Render the scene.
|
||
|
|
|
||
|
|
Background tensor (bg_color) must be on GPU!
|
||
|
|
"""
|
||
|
|
|
||
|
|
# Create zero tensor. We will use it to make pytorch return gradients of the 2D (screen-space) means
|
||
|
|
screenspace_points = torch.zeros_like(pc.get_xyz, dtype=pc.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=bg_color,
|
||
|
|
scale_modifier=scaling_modifier,
|
||
|
|
viewmatrix=viewpoint_camera.world_view_transform,
|
||
|
|
projmatrix=viewpoint_camera.full_proj_transform,
|
||
|
|
sh_degree=pc.active_sh_degree,
|
||
|
|
campos=viewpoint_camera.camera_center,
|
||
|
|
prefiltered=False
|
||
|
|
)
|
||
|
|
|
||
|
|
rasterizer = GaussianRasterizer(raster_settings=raster_settings)
|
||
|
|
|
||
|
|
means3D = pc.get_xyz
|
||
|
|
means2D = screenspace_points
|
||
|
|
opacity = pc.get_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 pipe.compute_cov3D_python:
|
||
|
|
cov3D_precomp = pc.get_covariance(scaling_modifier)
|
||
|
|
else:
|
||
|
|
scales = pc.get_scaling
|
||
|
|
rotations = pc.get_rotation
|
||
|
|
|
||
|
|
# 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 pipe.convert_SHs_python:
|
||
|
|
shs_view = pc.get_features.transpose(1, 2).view(-1, 3, (pc.max_sh_degree+1)**2)
|
||
|
|
dir_pp = (pc.get_xyz - viewpoint_camera.camera_center.repeat(pc.get_features.shape[0], 1))
|
||
|
|
dir_pp_normalized = dir_pp/dir_pp.norm(dim=1, keepdim=True)
|
||
|
|
sh2rgb = eval_sh(pc.active_sh_degree, shs_view, dir_pp_normalized)
|
||
|
|
colors_precomp = torch.clamp_min(sh2rgb + 0.5, 0.0)
|
||
|
|
else:
|
||
|
|
shs = pc.get_features
|
||
|
|
else:
|
||
|
|
colors_precomp = override_color
|
||
|
|
|
||
|
|
# Rasterize visible Gaussians to image, obtain their radii (on screen).
|
||
|
|
rendered_image, radii = rasterizer(
|
||
|
|
means3D = means3D,
|
||
|
|
means2D = means2D,
|
||
|
|
shs = shs,
|
||
|
|
colors_precomp = colors_precomp,
|
||
|
|
opacities = opacity,
|
||
|
|
scales = scales,
|
||
|
|
rotations = rotations,
|
||
|
|
cov3D_precomp = cov3D_precomp)
|
||
|
|
|
||
|
|
# 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 {"render": rendered_image,
|
||
|
|
"viewspace_points": screenspace_points,
|
||
|
|
"visibility_filter" : radii > 0,
|
||
|
|
"radii": radii}
|