UniqAI/gaussian-splatting
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mirror of https://github.com/graphdeco-inria/gaussian-splatting synced 2025-04-19 05:46:18 +00:00
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Add depth visualization

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ingra14m 2023-07-13 22:46:52 +08:00
parent 9f45a4f4e7
commit 13c6602e13
2 changed files with 11 additions and 3 deletions
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5
gaussian_renderer/__init__.py
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@ -81,7 +81,7 @@ def render(viewpoint_camera, pc : GaussianModel, pipe, bg_color : torch.Tensor,
colors_precomp = override_color colors_precomp = override_color
# Rasterize visible Gaussians to image, obtain their radii (on screen). # Rasterize visible Gaussians to image, obtain their radii (on screen).
rendered_image, radii = rasterizer( rendered_image, radii, depth = rasterizer(
means3D = means3D, means3D = means3D,
means2D = means2D, means2D = means2D,
shs = shs, shs = shs,
@ -96,4 +96,5 @@ def render(viewpoint_camera, pc : GaussianModel, pipe, bg_color : torch.Tensor,
return {"render": rendered_image, return {"render": rendered_image,
"viewspace_points": screenspace_points, "viewspace_points": screenspace_points,
"visibility_filter" : radii > 0, "visibility_filter" : radii > 0,
"radii": radii} "radii": radii,
"depth": depth}

9
render.py
View File

@ -24,15 +24,22 @@ from gaussian_renderer import GaussianModel
def render_set(model_path, name, iteration, views, gaussians, pipeline, background): def render_set(model_path, name, iteration, views, gaussians, pipeline, background):
render_path = os.path.join(model_path, name, "ours_{}".format(iteration), "renders") render_path = os.path.join(model_path, name, "ours_{}".format(iteration), "renders")
gts_path = os.path.join(model_path, name, "ours_{}".format(iteration), "gt") gts_path = os.path.join(model_path, name, "ours_{}".format(iteration), "gt")
depth_path = os.path.join(model_path, name, "ours_{}".format(iteration), "depth")
makedirs(render_path, exist_ok=True) makedirs(render_path, exist_ok=True)
makedirs(gts_path, exist_ok=True) makedirs(gts_path, exist_ok=True)
makedirs(depth_path, exist_ok=True)
for idx, view in enumerate(tqdm(views, desc="Rendering progress")): for idx, view in enumerate(tqdm(views, desc="Rendering progress")):
rendering = render(view, gaussians, pipeline, background)["render"] results = render(view, gaussians, pipeline, background)
rendering = results["render"]
depth = results["depth"]
depth = depth / (depth.max() + 1e-5)
gt = view.original_image[0:3, :, :] gt = view.original_image[0:3, :, :]
torchvision.utils.save_image(rendering, os.path.join(render_path, '{0:05d}'.format(idx) + ".png")) torchvision.utils.save_image(rendering, os.path.join(render_path, '{0:05d}'.format(idx) + ".png"))
torchvision.utils.save_image(gt, os.path.join(gts_path, '{0:05d}'.format(idx) + ".png")) torchvision.utils.save_image(gt, os.path.join(gts_path, '{0:05d}'.format(idx) + ".png"))
torchvision.utils.save_image(depth, os.path.join(depth_path, '{0:05d}'.format(idx) + ".png"))
def render_sets(dataset : ModelParams, iteration : int, pipeline : PipelineParams, skip_train : bool, skip_test : bool): def render_sets(dataset : ModelParams, iteration : int, pipeline : PipelineParams, skip_train : bool, skip_test : bool):
with torch.no_grad(): with torch.no_grad():