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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 [email protected]
#
import torch
import math
from scene.gaussian_model import GaussianModel
from utils.pose_utils import get_camera_from_tensor, quadmultiply
from utils.graphics_utils import depth_to_normal
### if use [diff-gaussian-rasterization](https://github.com/graphdeco-inria/diff-gaussian-rasterization)
# from diff_gaussian_rasterization import (
# GaussianRasterizationSettings,
# GaussianRasterizer,
# )
# 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,
# camera_pose=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)
# # Set camera pose as identity. Then, we will transform the Gaussians around camera_pose
# w2c = torch.eye(4).cuda()
# projmatrix = (
# w2c.unsqueeze(0).bmm(viewpoint_camera.projection_matrix.unsqueeze(0))
# ).squeeze(0)
# camera_pos = w2c.inverse()[3, :3]
# 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,
# viewmatrix=w2c,
# projmatrix=projmatrix,
# sh_degree=pc.active_sh_degree,
# # campos=viewpoint_camera.camera_center,
# campos=camera_pos,
# prefiltered=False,
# debug=pipe.debug,
# )
# rasterizer = GaussianRasterizer(raster_settings=raster_settings)
# # means3D = pc.get_xyz
# rel_w2c = get_camera_from_tensor(camera_pose)
# # Transform mean and rot of Gaussians to camera frame
# gaussians_xyz = pc._xyz.clone()
# gaussians_rot = pc._rotation.clone()
# xyz_ones = torch.ones(gaussians_xyz.shape[0], 1).cuda().float()
# xyz_homo = torch.cat((gaussians_xyz, xyz_ones), dim=1)
# gaussians_xyz_trans = (rel_w2c @ xyz_homo.T).T[:, :3]
# gaussians_rot_trans = quadmultiply(camera_pose[:4], gaussians_rot)
# means3D = gaussians_xyz_trans
# 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 = gaussians_rot_trans # 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 {
# "render": rendered_image,
# "viewspace_points": screenspace_points,
# "visibility_filter": radii > 0,
# "radii": radii,
# }
### if use [gsplat](https://github.com/nerfstudio-project/gsplat)
from gsplat import rasterization
def render_gsplat(
viewpoint_camera,
pc : GaussianModel,
pipe,
bg_color : torch.Tensor,
scaling_modifier = 1.0,
override_color = None,
camera_pose = None,
fov = None,
render_mode="RGB"):
"""
Render the scene.
Background tensor (bg_color) must be on GPU!
"""
if fov is None:
FoVx = viewpoint_camera.FoVx
FoVy = viewpoint_camera.FoVy
else:
FoVx = fov[0]
FoVy = fov[1]
tanfovx = math.tan(FoVx * 0.5)
tanfovy = math.tan(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="cuda",
)
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
if camera_pose is None:
viewmat = viewpoint_camera.world_view_transform.transpose(0, 1) # [4, 4]
else:
viewmat = get_camera_from_tensor(camera_pose)
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,
render_mode=render_mode,
)
if "D" in render_mode:
if "+" in render_mode:
depth_map = render_colors[..., -1:]
else:
depth_map = render_colors
normals_surf = depth_to_normal(
depth_map, torch.inverse(viewmat[None]), K[None])
normals_surf = normals_surf * (render_alphas).detach()
render_colors = torch.cat([render_colors, normals_surf], dim=-1)
# [1, H, W, 3] -> [3, H, W]
rendered_image = render_colors[0].permute(2, 0, 1)
radii = info["radii"].squeeze(0) # [N,]
try:
info["means2d"].retain_grad() # [1, N, 2]
except:
pass
# 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": info["means2d"],
"visibility_filter" : radii > 0,
"radii": radii}
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