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Running on 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 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, pipe, bg_color: torch.Tensor, scaling_modifier=1.0, override_color=None):
# lazy import
if "rasterization" not in globals():
from gsplat import rasterization
tanfovx = math.tan(viewpoint_camera.FoVx * 0.5)
tanfovy = math.tan(viewpoint_camera.FoVy * 0.5)
focal_length_x = viewpoint_camera.image_width / (2 * tanfovx)
focal_length_y = viewpoint_camera.image_height / (2 * tanfovy)
K = torch.tensor(
[
[focal_length_x, 0, viewpoint_camera.image_width / 2.0],
[0, focal_length_y, viewpoint_camera.image_height / 2.0],
[0, 0, 1],
],
device=pc.get_xyz.device,
dtype=torch.float32,
)
means3D = pc.get_xyz
opacity = pc.get_opacity
scales = pc.get_scaling * scaling_modifier
rotations = pc.get_rotation
if override_color is not None:
colors = override_color # [N, 3]
sh_degree = None
else:
colors = pc.get_features # [N, K, 3]
sh_degree = pc.active_sh_degree
viewmat = viewpoint_camera.world_view_transform.transpose(0, 1)
render_colors, render_alphas, info = rasterization(
means=means3D, # [N, 3]
quats=rotations, # [N, 4]
scales=scales, # [N, 3]
opacities=opacity.squeeze(-1), # [N]
colors=colors,
viewmats=viewmat[None], # [1, 4, 4]
Ks=K[None], # [1, 3, 3]
backgrounds=bg_color[None],
width=int(viewpoint_camera.image_width),
height=int(viewpoint_camera.image_height),
packed=False,
sh_degree=sh_degree,
rasterize_mode='antialiased'
)
rendered_image = render_colors[0].permute(2, 0, 1)
radii = info["radii"].squeeze(0)
try:
info["means2d"].retain_grad()
except Exception:
pass
return edict({
"render": rendered_image,
"viewspace_points": info["means2d"],
"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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