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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 math
import torch
from diff_LangSurf_rasterization import \
GaussianRasterizationSettings as PlaneGaussianRasterizationSettings
from diff_LangSurf_rasterization import \
GaussianRasterizer as PlaneGaussianRasterizer
from field_construction.scene.app_model import AppModel
from field_construction.scene.gaussian_model import GaussianModel
from field_construction.utils.graphics_utils import normal_from_depth_image
from field_construction.utils.pose_utils import (get_camera_from_tensor,
quadmultiply)
from field_construction.utils.sh_utils import eval_sh
def render_normal(viewpoint_cam, depth, offset=None, normal=None, scale=1):
# depth: (H, W), bg_color: (3), alpha: (H, W)
# normal_ref: (3, H, W)
intrinsic_matrix, extrinsic_matrix = viewpoint_cam.get_calib_matrix_nerf(scale=scale)
st = max(int(scale/2)-1,0)
if offset is not None:
offset = offset[st::scale,st::scale]
normal_ref = normal_from_depth_image(depth[st::scale,st::scale],
intrinsic_matrix.to(depth.device),
extrinsic_matrix.to(depth.device), offset)
normal_ref = normal_ref.permute(2,0,1)
return normal_ref
def render(
viewpoint_camera,
pc : GaussianModel,
pipe,
bg_color : torch.Tensor,
scaling_modifier=1.0,
override_color=None,
app_model: AppModel=None,
return_plane=True,
return_depth_normal=True,
include_feature=True,
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
screenspace_points_abs = torch.zeros_like(pc.get_xyz, dtype=pc.get_xyz.dtype, requires_grad=True, device="cuda") + 0
try:
screenspace_points.retain_grad()
screenspace_points_abs.retain_grad()
except:
pass
# Set up rasterization configuration
tanfovx = math.tan(viewpoint_camera.FoVx * 0.5)
tanfovy = math.tan(viewpoint_camera.FoVy * 0.5)
w2c = torch.eye(4).cuda()
projmatrix = (
w2c.unsqueeze(0).bmm(viewpoint_camera.projection_matrix.unsqueeze(0))
).squeeze(0)
camera_pos = w2c.inverse()[3, :3]
if camera_pose is not None:
rel_w2c = get_camera_from_tensor(camera_pose)
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
else:
means3D = pc.get_xyz
means2D = screenspace_points
means2D_abs = screenspace_points_abs
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 if camera_pose is not None else pc.get_rotation
# 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
if include_feature:
language_feature_precomp = pc.get_language_feature
instance_feature_precomp = pc.get_instance_feature
# language_feature_precomp = language_feature_precomp / (language_feature_precomp.norm(dim=-1, keepdim=True) + 1e-9)
# instance_feature_precomp = instance_feature_precomp / (instance_feature_precomp.norm(dim=-1, keepdim=True) + 1e-9)
# language_feature_precomp = torch.sigmoid(language_feature_precomp)
else:
language_feature_precomp = torch.zeros((1,), dtype=opacity.dtype, device=opacity.device)
instance_feature_precomp = torch.zeros((1,), dtype=opacity.dtype, device=opacity.device)
return_dict = None
raster_settings = PlaneGaussianRasterizationSettings(
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 if camera_pose is not None else viewpoint_camera.world_view_transform,
projmatrix=projmatrix if camera_pose is not None else viewpoint_camera.full_proj_transform,
sh_degree=pc.active_sh_degree,
# campos=viewpoint_camera.camera_center,
campos=camera_pos if camera_pose is not None else viewpoint_camera.camera_center,
prefiltered=False,
render_geo=return_plane,
debug=pipe.debug,
include_feature=include_feature,
)
rasterizer = PlaneGaussianRasterizer(raster_settings=raster_settings)
if not return_plane:
rendered_image, language_feature, instance_feature, radii, out_observe, _, _ = rasterizer(
means3D = means3D,
means2D = means2D,
means2D_abs = means2D_abs,
shs = shs,
colors_precomp = colors_precomp,
language_feature_precomp = language_feature_precomp,
language_feature_instance_precomp = instance_feature_precomp,
opacities = opacity,
scales = scales,
rotations = rotations,
cov3D_precomp = cov3D_precomp)
return_dict = {"render": rendered_image,
"viewspace_points": screenspace_points,
"viewspace_points_abs": screenspace_points_abs,
"visibility_filter" : radii > 0,
"radii": radii,
"out_observe": out_observe,
"language_feature": language_feature,
"instance_feature": instance_feature,
}
if app_model is not None and pc.use_app:
appear_ab = app_model.appear_ab[torch.tensor(viewpoint_camera.uid).cuda()]
app_image = torch.exp(appear_ab[0]) * rendered_image + appear_ab[1]
return_dict.update({"app_image": app_image})
return return_dict
global_normal = pc.get_normal(viewpoint_camera)
local_normal = global_normal @ viewpoint_camera.world_view_transform[:3,:3]
pts_in_cam = means3D @ viewpoint_camera.world_view_transform[:3,:3] + viewpoint_camera.world_view_transform[3,:3]
depth_z = pts_in_cam[:, 2]
local_distance = (local_normal * pts_in_cam).sum(-1).abs()
input_all_map = torch.zeros((means3D.shape[0], 5)).cuda().float()
input_all_map[:, :3] = local_normal
input_all_map[:, 3] = 1.0
input_all_map[:, 4] = local_distance
rendered_image, language_feature, instance_feature, radii, out_observe, out_all_map, plane_depth = rasterizer(
means3D = means3D,
means2D = means2D,
means2D_abs = means2D_abs,
shs = shs,
colors_precomp = colors_precomp,
language_feature_precomp = language_feature_precomp,
language_feature_instance_precomp = instance_feature_precomp,
opacities = opacity,
scales = scales,
rotations = rotations,
all_map = input_all_map,
cov3D_precomp = cov3D_precomp)
rendered_normal = out_all_map[0:3]
rendered_alpha = out_all_map[3:4, ]
rendered_distance = out_all_map[4:5, ]
return_dict = {"render": rendered_image,
"viewspace_points": screenspace_points,
"viewspace_points_abs": screenspace_points_abs,
"visibility_filter" : radii > 0,
"radii": radii,
"out_observe": out_observe,
"rendered_normal": rendered_normal,
"plane_depth": plane_depth,
"rendered_distance": rendered_distance,
"language_feature": language_feature,
"instance_feature": instance_feature,
}
if app_model is not None:
appear_ab = app_model.appear_ab[torch.tensor(viewpoint_camera.uid).cuda()]
app_image = torch.exp(appear_ab[0]) * rendered_image + appear_ab[1]
return_dict.update({"app_image": app_image})
if return_depth_normal:
depth_normal = render_normal(viewpoint_camera, plane_depth.squeeze()) * (rendered_alpha).detach()
return_dict.update({"depth_normal": depth_normal})
# 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 return_dict |