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Running
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Zero
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#
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
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, kernel_size, scaling_modifier = 1.0, require_coord : bool = True, require_depth : bool = True):
"""
Render the scene.
Background tensor (bg_color) must be on GPU!
"""
# Set up rasterization configuration
tanfovx = math.tan(viewpoint_camera.FoVx * 0.5)
tanfovy = math.tan(viewpoint_camera.FoVy * 0.5)
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
raster_settings = GaussianRasterizationSettings(
image_height=int(viewpoint_camera.image_height),
image_width=int(viewpoint_camera.image_width),
tanfovx=tanfovx,
tanfovy=tanfovy,
kernel_size = kernel_size,
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,
require_coord = require_coord,
require_depth = require_depth,
debug=pipe.debug
)
rasterizer = GaussianRasterizer(raster_settings=raster_settings)
means3D = pc.get_xyz
means2D = screenspace_points
# 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
scales, opacity = pc.get_scaling_n_opacity_with_3D_filter
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 = pc.get_features
colors_precomp = None
rendered_image, radii, rendered_expected_coord, rendered_median_coord, rendered_expected_depth, rendered_median_depth, rendered_alpha, rendered_normal = 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,
"mask": rendered_alpha,
"expected_coord": rendered_expected_coord,
"median_coord": rendered_median_coord,
"expected_depth": rendered_expected_depth,
"median_depth": rendered_median_depth,
"viewspace_points": means2D,
"visibility_filter" : radii > 0,
"radii": radii,
"normal":rendered_normal,
}
# integration is adopted from GOF for marching tetrahedra https://github.com/autonomousvision/gaussian-opacity-fields/blob/main/gaussian_renderer/__init__.py
def integrate(points3D, viewpoint_camera, pc : GaussianModel, pipe, bg_color : torch.Tensor, kernel_size : float, scaling_modifier = 1.0, override_color = None):
"""
integrate Gaussians to the points, we also render the image for visual comparison.
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,
kernel_size = kernel_size,
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,
debug=pipe.debug,
require_depth = True,
require_coord=True
)
rasterizer = GaussianRasterizer(raster_settings=raster_settings)
means3D = pc.get_xyz
means2D = screenspace_points
opacity = pc.get_opacity_with_3D_filter
# 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_with_3D_filter
rotations = pc.get_rotation
depth_plane_precomp = None
# 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 override_color 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))
# # we local direction
# cam_pos_local = view2gaussian_precomp[:, 3, :3]
# cam_pos_local_scaled = cam_pos_local / scales
# dir_pp = -cam_pos_local_scaled
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, alpha_integrated, color_integrated, point_coordinate, point_sdf, radii = rasterizer.integrate(
points3D = points3D,
means3D = means3D,
means2D = means2D,
shs = shs,
colors_precomp = colors_precomp,
opacities = opacity,
scales = scales,
rotations = rotations,
cov3D_precomp = cov3D_precomp,
view2gaussian_precomp=depth_plane_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,
"alpha_integrated": alpha_integrated,
"color_integrated": color_integrated,
"point_coordinate": point_coordinate,
"point_sdf": point_sdf,
"visibility_filter" : radii > 0,
"radii": radii} |