Function render_gaussians

src/sharp/cli/render.py:70–120

Render a single gaussian checkpoint file.

(
    gaussians: Gaussians3D,
    metadata: SceneMetaData,
    output_path: Path,
    params: camera.TrajectoryParams | None = None,
)

def render_gaussians(
    gaussians: Gaussians3D,
    metadata: SceneMetaData,
    output_path: Path,
    params: camera.TrajectoryParams | None = None,
) -> None:
    """Render a single gaussian checkpoint file."""
    (width, height) = metadata.resolution_px
    f_px = metadata.focal_length_px

    if params is None:
        params = camera.TrajectoryParams()

    if not torch.cuda.is_available():
        raise RuntimeError("Rendering a checkpoint requires CUDA.")

    device = torch.device("cuda")

    intrinsics = torch.tensor(
        [
            [f_px, 0, (width - 1) / 2.0, 0],
            [0, f_px, (height - 1) / 2.0, 0],
            [0, 0, 1, 0],
            [0, 0, 0, 1],
        ],
        device=device,
        dtype=torch.float32,
    )
    camera_model = camera.create_camera_model(
        gaussians, intrinsics, resolution_px=metadata.resolution_px
    )

    trajectory = camera.create_eye_trajectory(
        gaussians, params, resolution_px=metadata.resolution_px, f_px=f_px
    )
    renderer = gsplat.GSplatRenderer(color_space=metadata.color_space)
    video_writer = io.VideoWriter(output_path)

    for _, eye_position in enumerate(trajectory):
        camera_info = camera_model.compute(eye_position)
        rendering_output = renderer(
            gaussians.to(device),
            extrinsics=camera_info.extrinsics[None].to(device),
            intrinsics=camera_info.intrinsics[None].to(device),
            image_width=camera_info.width,
            image_height=camera_info.height,
        )
        color = (rendering_output.color[0].permute(1, 2, 0) * 255.0).to(dtype=torch.uint8)
        depth = rendering_output.depth[0]
        video_writer.add_frame(color, depth)
    video_writer.close()