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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 numpy as np
import torch
import math
from diff_gaussian_rasterization import GaussianRasterizationSettings, GaussianRasterizer
from .gaussian_model import GaussianModel
from .sh_utils import eval_sh
from .graphics_utils import getWorld2View2, getProjectionMatrix
class DummyCamera:
def __init__(self, R, T, FoVx, FoVy, W, H):
self.projection_matrix = getProjectionMatrix(znear=0.01, zfar=100.0, fovX=FoVx, fovY=FoVy).transpose(0,1).cuda()
self.R = R
self.T = T
self.world_view_transform = torch.tensor(getWorld2View2(R, T, np.array([0,0,0]), 1.0)).transpose(0, 1).cuda()
self.full_proj_transform = (self.world_view_transform.unsqueeze(0).bmm(self.projection_matrix.unsqueeze(0))).squeeze(0)
self.camera_center = self.world_view_transform.inverse()[3, :3]
self.image_width = W
self.image_height = H
self.FoVx = FoVx
self.FoVy = FoVy
class DummyPipeline:
convert_SHs_python = False
compute_cov3D_python = False
debug = False
def calculate_fov(output_width, output_height, focal_length, aspect_ratio=1.0, invert_y=False):
fovx = 2 * math.atan((output_width / (2 * focal_length)))
fovy = 2 * math.atan((output_height / aspect_ratio) / (2 * focal_length))
if invert_y:
fovy = -fovy
return fovx, fovy
# def render(viewpoint_camera, pc : GaussianModel, pipe, bg_color : torch.Tensor, scaling_modifier = 1.0, override_color = 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)
# 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,
# 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 {"render": rendered_image,
# "viewspace_points": screenspace_points,
# "visibility_filter" : radii > 0,
# "radii": radii}
def render(viewpoint_camera, pc : GaussianModel, pipe, bg_color : torch.Tensor, scaling_modifier = 1.0, override_color = 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)
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,
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
semantic_feature = pc.get_semantic_feature
# Rasterize visible Gaussians to image, obtain their radii (on screen).
rendered_image, feature_map, radii, depth = rasterizer(
means3D = means3D,
means2D = means2D,
shs = shs,
colors_precomp = colors_precomp,
semantic_feature = semantic_feature,
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,
'feature_map': feature_map,
"depth": depth} ###d |