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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 easydict import EasyDict as edict
import numpy as np
from ..representations.gaussian import Gaussian
from .sh_utils import eval_sh
import torch.nn.functional as F
from easydict import EasyDict as edict
def intrinsics_to_projection(
intrinsics: torch.Tensor,
near: float,
far: float,
) -> torch.Tensor:
"""
OpenCV intrinsics to OpenGL perspective matrix
Args:
intrinsics (torch.Tensor): [3, 3] OpenCV intrinsics matrix
near (float): near plane to clip
far (float): far plane to clip
Returns:
(torch.Tensor): [4, 4] OpenGL perspective matrix
"""
fx, fy = intrinsics[0, 0], intrinsics[1, 1]
cx, cy = intrinsics[0, 2], intrinsics[1, 2]
ret = torch.zeros((4, 4), dtype=intrinsics.dtype, device=intrinsics.device)
ret[0, 0] = 2 * fx
ret[1, 1] = 2 * fy
ret[0, 2] = 2 * cx - 1
ret[1, 2] = - 2 * cy + 1
ret[2, 2] = far / (far - near)
ret[2, 3] = near * far / (near - far)
ret[3, 2] = 1.
return ret
def render(viewpoint_camera, pc : Gaussian, pipe, bg_color : torch.Tensor, scaling_modifier = 1.0, override_color = None):
"""
Render the scene.
Background tensor (bg_color) must be on GPU!
"""
# lazy import
if 'GaussianRasterizer' not in globals():
from diff_gaussian_rasterization import GaussianRasterizer, GaussianRasterizationSettings
# 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)
kernel_size = pipe.kernel_size
subpixel_offset = torch.zeros((int(viewpoint_camera.image_height), int(viewpoint_camera.image_width), 2), dtype=torch.float32, device="cuda")
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,
subpixel_offset=subpixel_offset,
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
)
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 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))
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 edict({"render": rendered_image,
"viewspace_points": screenspace_points,
"visibility_filter" : radii > 0,
"radii": radii})
class GaussianRenderer:
"""
Renderer for the Voxel representation.
Args:
rendering_options (dict): Rendering options.
"""
def __init__(self, rendering_options={}) -> None:
self.pipe = edict({
"kernel_size": 0.1,
"convert_SHs_python": False,
"compute_cov3D_python": False,
"scale_modifier": 1.0,
"debug": False
})
self.rendering_options = edict({
"resolution": None,
"near": None,
"far": None,
"ssaa": 1,
"bg_color": 'random',
})
self.rendering_options.update(rendering_options)
self.bg_color = None
def render(
self,
gausssian: Gaussian,
extrinsics: torch.Tensor,
intrinsics: torch.Tensor,
colors_overwrite: torch.Tensor = None
) -> edict:
"""
Render the gausssian.
Args:
gaussian : gaussianmodule
extrinsics (torch.Tensor): (4, 4) camera extrinsics
intrinsics (torch.Tensor): (3, 3) camera intrinsics
colors_overwrite (torch.Tensor): (N, 3) override color
Returns:
edict containing:
color (torch.Tensor): (3, H, W) rendered color image
"""
resolution = self.rendering_options["resolution"]
near = self.rendering_options["near"]
far = self.rendering_options["far"]
ssaa = self.rendering_options["ssaa"]
if self.rendering_options["bg_color"] == 'random':
self.bg_color = torch.zeros(3, dtype=torch.float32, device="cuda")
if np.random.rand() < 0.5:
self.bg_color += 1
else:
self.bg_color = torch.tensor(self.rendering_options["bg_color"], dtype=torch.float32, device="cuda")
view = extrinsics
perspective = intrinsics_to_projection(intrinsics, near, far)
camera = torch.inverse(view)[:3, 3]
focalx = intrinsics[0, 0]
focaly = intrinsics[1, 1]
fovx = 2 * torch.atan(0.5 / focalx)
fovy = 2 * torch.atan(0.5 / focaly)
camera_dict = edict({
"image_height": resolution * ssaa,
"image_width": resolution * ssaa,
"FoVx": fovx,
"FoVy": fovy,
"znear": near,
"zfar": far,
"world_view_transform": view.T.contiguous(),
"projection_matrix": perspective.T.contiguous(),
"full_proj_transform": (perspective @ view).T.contiguous(),
"camera_center": camera
})
# Render
render_ret = render(camera_dict, gausssian, self.pipe, self.bg_color, override_color=colors_overwrite, scaling_modifier=self.pipe.scale_modifier)
if ssaa > 1:
render_ret.render = F.interpolate(render_ret.render[None], size=(resolution, resolution), mode='bilinear', align_corners=False, antialias=True).squeeze()
ret = edict({
'color': render_ret['render']
})
return ret
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