Spaces:
Running
on
Zero
Running
on
Zero
File size: 8,122 Bytes
db6a3b7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 |
#
# 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
|