mirror of
https://github.com/graphdeco-inria/gaussian-splatting
synced 2025-02-19 18:48:40 +00:00
18eb6d6a0c
Users may want to reduce their memory consumption by using fp16. However, in my tests, such attempts will result in lower quality renders. Some data type conversions did not have any impact, so I removed them completely.
105 lines
3.9 KiB
Python
105 lines
3.9 KiB
Python
#
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# Copyright (C) 2023, Inria
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# GRAPHDECO research group, https://team.inria.fr/graphdeco
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# All rights reserved.
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#
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# This software is free for non-commercial, research and evaluation use
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# under the terms of the LICENSE.md file.
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#
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# For inquiries contact george.drettakis@inria.fr
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#
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import torch
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import math
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from diff_gaussian_rasterization import GaussianRasterizationSettings, GaussianRasterizer
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from scene.gaussian_model import GaussianModel
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from utils.sh_utils import eval_sh
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def render(viewpoint_camera, pc : GaussianModel, pipe, bg_color : torch.Tensor, scaling_modifier = 1.0, override_color = None):
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"""
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Render the scene.
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Background tensor (bg_color) must be on GPU!
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"""
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# Create zero tensor. We will use it to make pytorch return gradients of the 2D (screen-space) means
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screenspace_points = torch.zeros_like(pc.get_xyz, dtype=pc.get_xyz.dtype, requires_grad=True, device="cuda") + 0
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try:
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screenspace_points.retain_grad()
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except:
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pass
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# Set up rasterization configuration
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tanfovx = math.tan(viewpoint_camera.FoVx * 0.5)
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tanfovy = math.tan(viewpoint_camera.FoVy * 0.5)
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raster_settings = GaussianRasterizationSettings(
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image_height=int(viewpoint_camera.image_height),
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image_width=int(viewpoint_camera.image_width),
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tanfovx=tanfovx,
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tanfovy=tanfovy,
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bg=bg_color,
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scale_modifier=scaling_modifier,
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viewmatrix=viewpoint_camera.world_view_transform,
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projmatrix=viewpoint_camera.full_proj_transform,
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sh_degree=pc.active_sh_degree,
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campos=viewpoint_camera.camera_center,
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prefiltered=False,
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debug=pipe.debug
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)
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rasterizer = GaussianRasterizer(raster_settings=raster_settings)
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means3D = pc.get_xyz
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means2D = screenspace_points
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opacity = pc.get_opacity
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# If precomputed 3d covariance is provided, use it. If not, then it will be computed from
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# scaling / rotation by the rasterizer.
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scales = None
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rotations = None
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cov3D_precomp = None
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if pipe.compute_cov3D_python:
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cov3D_precomp = pc.get_covariance(scaling_modifier)
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else:
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scales = pc.get_scaling
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rotations = pc.get_rotation
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# If precomputed colors are provided, use them. Otherwise, if it is desired to precompute colors
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# from SHs in Python, do it. If not, then SH -> RGB conversion will be done by rasterizer.
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shs = None
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colors_precomp = None
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if override_color is None:
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if pipe.convert_SHs_python:
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shs_view = pc.get_features.transpose(1, 2).view(-1, 3, (pc.max_sh_degree+1)**2)
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dir_pp = (pc.get_xyz - viewpoint_camera.camera_center.repeat(pc.get_features.shape[0], 1))
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dir_pp_normalized = dir_pp/dir_pp.norm(dim=1, keepdim=True)
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sh2rgb = eval_sh(pc.active_sh_degree, shs_view, dir_pp_normalized)
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colors_precomp = torch.clamp_min(sh2rgb + 0.5, 0.0)
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else:
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shs = pc.get_features
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else:
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colors_precomp = override_color
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# Rasterize visible Gaussians to image, obtain their radii (on screen).
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rendered_image, radii = rasterizer(
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means3D = means3D,
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means2D = means2D,
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shs = shs,
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colors_precomp = colors_precomp,
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opacities = opacity,
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scales = scales,
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rotations = rotations,
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cov3D_precomp = cov3D_precomp)
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# after rasterization, we convert the resulting image to the target dtype
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# The rasterizer expects parameters as float32, so the result is also float32.
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rendered_image = rendered_image.to(viewpoint_camera.original_image.dtype)
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# Those Gaussians that were frustum culled or had a radius of 0 were not visible.
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# They will be excluded from value updates used in the splitting criteria.
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return {"render": rendered_image,
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"viewspace_points": screenspace_points,
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"visibility_filter" : radii > 0,
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"radii": radii}
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