gsplat offers many extra features, including batch rasterization, N-D feature rendering, depth rendering, sparse gradient, multi-GPU distributed rasterization etc.

Default is False. key_for_gradient (str): Which variable uses for densification strategy. 3DGS uses "means2d" gradient and 2DGS uses a similar gradient which stores in variable "gradient_2dgs".

>>> # define Gaussians >>> means = torch.randn((100, 3), device=device) >>> quats = torch.randn((100, 4), device=device) >>> scales = torch.rand((100, 3), device=device) * 0.1 …

Examples: .. code-block:: python >>> # define Gaussians >>> means = torch.randn ( (100, 3), device=device) >>> quats = torch.randn ( (100, 4), device=device) >>> scales = torch.rand ( …

See :doc:`examples/large_scale` for an example. * *gsplat* offers many extra features, including **batch rasterization**, **N-D feature rendering**, **depth rendering**, **sparse gradient**, …

On top of that, there are more functionalities supported in gsplat, including batched rasterization, trade-off between memory and speed, sparse gradient etc. Additionally, we also provide a …

key_for_gradient (str) – Which variable uses for densification strategy. 3DGS uses “means2d” gradient and 2DGS uses a similar gradient which stores in variable “gradient_2dgs”.

densify – Dummy variable to keep track of gradient for densification. […, N, 2] if packed, [nnz, 3] if packed is True. tile_size – Tile size. isect_offsets – Intersection offsets outputs from …

N, channels] if packed is False, [nnz, channels] if packed is True. opacities: Gaussian opacities that support per-view values. [..., N] if packed is False, [nnz] if packed is True. normals: The …