gsplat API reference#
2025-09-26
30 min read time
This topic contains the API reference of the supported features of gsplat.
Supported features#
Rasterization#
3DGS#
rasterization(
means: Tensor, quats: Tensor, scales: Tensor, opacities: Tensor, colors: Tensor,
viewmats: Tensor, Ks: Tensor, width: int, height: int,
near_plane: float = 0.01, far_plane: float = 1e10,
radius_clip: float = 0.0, eps2d: float = 0.3,
sh_degree: int | None = None, packed: bool = True, tile_size: int = 16,
backgrounds: Tensor | None = None,
render_mode: Literal["RGB","D","ED","RGB+D","RGB+ED"] = "RGB",
sparse_grad: bool = False, absgrad: bool = False,
rasterize_mode: Literal["classic","antialiased"] = "classic",
channel_chunk: int = 32, distributed: bool = False,
camera_model: Literal["pinhole","ortho","fisheye","ftheta"] = "pinhole",
segmented: bool = False, covars: Tensor | None = None,
with_ut: bool = False, with_eval3d: bool = False,
radial_coeffs: Tensor | None = None, tangential_coeffs: Tensor | None = None,
thin_prism_coeffs: Tensor | None = None,
ftheta_coeffs: FThetaCameraDistortionParameters | None = None,
rolling_shutter: RollingShutterType = RollingShutterType.GLOBAL,
viewmats_rs: Tensor | None = None
) -> Tuple[Tensor, Tensor, Dict]
Function: Rasterizes 3D Gaussians (N) into a batch of image planes (C).
Returns:
render_colors: The rendered colors. […, C, height, width, X]. X depends on the render_mode and input colors. If render_mode is “RGB”, X is D; if render_mode is “D” or “ED”, X is 1; if render_mode is “RGB+D” or “RGB+ED”, X is D+1.
render_alphas: The rendered alphas. […, C, height, width, 1].
meta: A dictionary of intermediate results of the rasterization.
Return type: tuple
Parameters:
means – The 3D centers of the Gaussians. […, N, 3]
quats – The quaternions of the Gaussians (wxyz convension). It’s not required to be normalized. […, N, 4]
scales – The scales of the Gaussians. […, N, 3]
opacities – The opacities of the Gaussians. […, N]
colors – The colors of the Gaussians. […, (C,) N, D] or […, (C,) N, K, 3] for SH coefficients.
viewmats – The world-to-cam transformation of the cameras. […, C, 4, 4]
Ks – The camera intrinsics. […, C, 3, 3]
width – The width of the image.
height – The height of the image.
near_plane – The near plane for clipping. Default is 0.01.
far_plane – The far plane for clipping. Default is 1e10.
radius_clip – Gaussians with 2D radius smaller or equal than this value will be skipped. This is extremely helpful for speeding up large scale scenes. Default is 0.0.
eps2d – An epsilon added to the egienvalues of projected 2D covariance matrices. This will prevents the projected GS to be too small. For example eps2d=0.3 leads to minimal 3 pixel unit. Default is 0.3.
sh_degree – The SH degree to use, which can be smaller than the total number of bands. If set, the colors should be […, (C,) N, K, 3] SH coefficients, else the colors should be […, (C,) N, D] post-activation color values. Default is None.
packed – Whether to use packed mode which is more memory efficient but might or might not be as fast. Default is True.
tile_size – The size of the tiles for rasterization. Default is 16. (Note: other values are not tested)
backgrounds – The background colors. […, C, D]. Default is None.
render_mode – The rendering mode. Supported modes are “RGB”, “D”, “ED”, “RGB+D”, and “RGB+ED”. “RGB” renders the colored image, “D” renders the accumulated depth, and “ED” renders the expected depth. Default is “RGB”.
sparse_grad – If true, the gradients for {means, quats, scales} will be stored in a COO sparse layout. This can be helpful for saving memory. Default is False.
absgrad – If true, the absolute gradients of the projected 2D means will be computed during the backward pass, which could be accessed by meta[“means2d”].absgrad. Default is False.
rasterize_mode – The rasterization mode. Supported modes are “classic” and “antialiased”. Default is “classic”.
channel_chunk – The number of channels to render in one go. Default is 32. If the required rendering channels are larger than this value, the rendering will be done looply in chunks.
distributed – Whether to use distributed rendering. Default is False. If True, The input Gaussians are expected to be a subset of scene in each rank, and the function will collaboratively render the images for all ranks.
camera_model – The camera model to use. Supported models are “pinhole”, “ortho”, “fisheye”, and “ftheta”. Default is “pinhole”.
segmented – Whether to use segmented radix sort. Default is False. Segmented radix sort performs sorting in segments, which is more efficient for the sorting operation itself. However, since it requires offset indices as input, additional global memory access is needed, which results in slower overall performance in most use cases.
covars – Optional covariance matrices of the Gaussians. If provided, the quats and scales will be ignored. […, N, 3, 3], Default is None.
with_ut – Whether to use Unscented Transform (UT) for projection. Default is False.
with_eval3d – Whether to calculate Gaussian response in 3D world space, instead of 2D image space. Default is False.
radial_coeffs – Opencv pinhole/fisheye radial distortion coefficients. Default is None. For pinhole camera, the shape should be […, C, 6]. For fisheye camera, the shape should be […, C, 4].
tangential_coeffs – Opencv pinhole tangential distortion coefficients. Default is None. The shape should be […, C, 2] if provided.
thin_prism_coeffs – Opencv pinhole thin prism distortion coefficients. Default is None. The shape should be […, C, 4] if provided.
ftheta_coeffs – F-Theta camera distortion coefficients shared for all cameras. Default is None. See FThetaCameraDistortionParameters for details.
rolling_shutter – The rolling shutter type. Default RollingShutterType.GLOBAL means global shutter.
viewmats_rs – The second viewmat when rolling shutter is used. Default is None.
Notes#
This function is currently not differentiable with regard to the camera intrinsics Ks.
Multi-GPU Distributed Rasterization: This function can be used in a multi-GPU distributed scenario by setting distributed to True. When distributed is True, a subset of total Gaussians could be passed into this function in each rank, and the function will collaboratively render a set of images using Gaussians from all ranks. Note to achieve balanced computation, it is recommended (not enforced) to have similar number of Gaussians in each rank. But it is enforced that the number of cameras to be rendered in each rank is the same. The function will return the rendered images corresponds to the input cameras in each rank, and allows for gradients to flow back to the Gaussians living in other ranks. For the details, please refer to the paper On Scaling Up 3D Gaussian Splatting Training.
Batch Rasterization: This function allows for rasterizing a set of 3D Gaussians to a batch of images in one go, by simplly providing the batched viewmats and Ks.
Support N-D Features: If sh_degree is None, the colors is expected to be with shape […, N, D] or […, C, N, D], in which D is the channel of the features to be rendered. The computation is slow when D > 32 at the moment. If sh_degree is set, the colors is expected to be the SH coefficients with shape […, N, K, 3] or […, C, N, K, 3], where K is the number of SH bases.
Depth Rendering: This function supports colors or/and depths via render_mode. The supported modes are “RGB”, “D”, “ED”, “RGB+D”, and “RGB+ED”. “RGB” renders the colored image that respects the colors argument. “D” renders the accumulated z-depth “ED” renders the expected z-depth. “RGB+D” and “RGB+ED” render both the colored image and the depth, in which the depth is the last channel of the output.
Memory-Speed Trade-off: The packed argument provides a trade-off between memory footprint and runtime. If packed is True, the intermediate results are packed into sparse tensors, which is more memory efficient but might be slightly slower. This is especially helpful when the scene is large and each camera sees only a small portion of the scene. If packed is False, the intermediate results are with shape […, C, N, …], which is faster but might consume more memory.
Sparse Gradients: If sparse_grad is True, the gradients for {means, quats, scales} will be stored in a COO sparse layout. This can be helpful for saving memory for training when the scene is large and each iteration only activates a small portion of the Gaussians. Usually a sparse optimizer is required to work with sparse gradients, such as torch.optim.SparseAdam. This argument is only effective when packed is True.
Speed-up for Large Scenes: The radius_clip argument is extremely helpful for speeding up large scale scenes or scenes with large depth of fields. Gaussians with 2D radius smaller or equal than this value (in pixel unit) will be skipped during rasterization. This will skip all the far-away Gaussians that are too small to be seen in the image. But be warned that if there are close-up Gaussians that are also below this threshold, they will also get skipped (which is rarely happened in practice). This is by default disabled by setting radius_clip to 0.0.
Antialiased Rendering: If rasterize_mode is “antialiased”, the function will apply a view-dependent compensation factor to Gaussian opacities, where is the projected 2D covariance matrix and is the eps2d. This will make the rendered image more antialiased, as proposed in the paper Mip-Splatting: Alias-free 3D Gaussian Splatting.
AbsGrad: If absgrad is True, the absolute gradients of the projected 2D means will be computed during the backward pass, which could be accessed by meta[“means2d”].absgrad. This is an implementation of the paper AbsGS: Recovering Fine Details for 3D Gaussian Splatting, which is shown to be more effective for splitting Gaussians during training.
Camera Distortion and Rolling Shutter: The function supports rendering with opencv distortion formula for pinhole and fisheye cameras (radial_coeffs, tangential_coeffs, thin_prism_coeffs). It also supports rolling shutter rendering with the rolling_shutter argument. This is referenced from the paper 3DGUT: Enabling Distorted Cameras and Secondary Rays in Gaussian Splatting.
2DGS#
rasterization_2dgs(
means: Tensor, quats: Tensor, scales: Tensor, opacities: Tensor, colors: Tensor,
viewmats: Tensor, Ks: Tensor, width: int, height: int,
near_plane: float = 0.01, far_plane: float = 1e10,
radius_clip: float = 0.0, eps2d: float = 0.3,
sh_degree: int | None = None, packed: bool = False, tile_size: int = 16,
backgrounds: Tensor | None = None,
render_mode: Literal["RGB","D","ED","RGB+D","RGB+ED"] = "RGB",
sparse_grad: bool = False, absgrad: bool = False,
distloss: bool = False, depth_mode: Literal["expected","median"] = "expected"
) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Dict]
Function: Rasterizes a set of 2D Gaussians (N) to a batch of image planes (C). This function supports a handful of features, similar to the rasterization() function.
Returns:
render_colors: The rendered colors. […, C, height, width, X]. X depends on the render_mode and input colors. If render_mode is “RGB”, X is D; if render_mode is “D” or “ED”, X is 1; if render_mode is “RGB+D” or “RGB+ED”, X is D+1.
render_alphas: The rendered alphas. […, C, height, width, 1].
render_normals: The rendered normals. […, C, height, width, 3].
surf_normals: surface normal from depth. […, C, height, width, 3]
render_distort: The rendered distortions. […, C, height, width, 1]. L1 version, different from L2 version in 2DGS paper.
render_median: The rendered median depth. […, C, height, width, 1].
meta: A dictionary of intermediate results of the rasterization.
Return type: tuple
Parameters:
means – The 3D centers of the Gaussians. […, N, 3]
quats – The quaternions of the Gaussians (wxyz convension). It’s not required to be normalized. […, N, 4]
scales – The scales of the Gaussians. […, N, 3]
opacities – The opacities of the Gaussians. […, N]
colors – The colors of the Gaussians. […, (C,) N, D] or […, (C,) N, K, 3] for SH coefficients.
viewmats – The world-to-cam transformation of the cameras. […, C, 4, 4]
Ks – The camera intrinsics. […, C, 3, 3]
width – The width of the image.
height – The height of the image.
near_plane – The near plane for clipping. Default is 0.01.
far_plane – The far plane for clipping. Default is 1e10.
radius_clip – Gaussians with 2D radius smaller or equal than this value will be skipped. This is extremely helpful for speeding up large scale scenes. Default is 0.0.
eps2d – An epsilon added to the egienvalues of projected 2D covariance matrices. This will prevents the projected GS to be too small. For example eps2d=0.3 leads to minimal 3 pixel unit. Default is 0.3.
sh_degree – The SH degree to use, which can be smaller than the total number of bands. If set, the colors should be [(C,) N, K, 3] SH coefficients, else the colors should [(C,) N, D] post-activation color values. Default is None.
packed – Whether to use packed mode which is more memory efficient but might or might not be as fast. Default is True.
tile_size – The size of the tiles for rasterization. Default is 16. (Note: other values are not tested)
backgrounds – The background colors. [C, D]. Default is None.
render_mode – The rendering mode. Supported modes are “RGB”, “D”, “ED”, “RGB+D”, and “RGB+ED”. “RGB” renders the colored image, “D” renders the accumulated depth, and “ED” renders the expected depth. Default is “RGB”.
sparse_grad (Experimental) – If true, the gradients for {means, quats, scales} will be stored in a COO sparse layout. This can be helpful for saving memory. Default is False.
absgrad – If true, the absolute gradients of the projected 2D means will be computed during the backward pass, which could be accessed by meta[“means2d”].absgrad. Default is False.
channel_chunk – The number of channels to render in one go. Default is 32. If the required rendering channels are larger than this value, the rendering will be done looply in chunks.
distloss – If true, use distortion regularization to get better geometry detail.
depth_mode – render depth mode. Choose from expected depth and median depth.
Notes#
This function is currently not differentiable with regard to the camera intrinsics Ks.
Densification#
In gsplat, you can abstract out the densification and pruning process of the Gaussian training into a strategy. A strategy is a class that defines how the Gaussian parameters (along with their optimizers) should be updated (splitting, pruning, etc.) during the training.
Default strategy#
class DefaultStrategy(
prune_opa: float = 0.005, grow_grad2d: float = 0.0002,
grow_scale3d: float = 0.01, grow_scale2d: float = 0.05,
prune_scale3d: float = 0.1, prune_scale2d: float = 0.15,
refine_scale2d_stop_iter: int = 0, refine_start_iter: int = 500,
refine_stop_iter: int = 15000, reset_every: int = 3000,
refine_every: int = 100, pause_refine_after_reset: int = 0,
absgrad: bool = False, revised_opacity: bool = False,
verbose: bool = False,
key_for_gradient: Literal["means2d","gradient_2dgs"] = "means2d"
)
A default strategy that follows the original 3DGS paper. The strategy will:
Periodically duplicate GSs with high image plane gradients and small scales.
Periodically split GSs with high image plane gradients and large scales.
Periodically prune GSs with low opacity.
Periodically reset GSs to a lower opacity.
Parameters:
prune_opa (float) – GSs with opacity below this value will be pruned. Default is 0.005.
grow_grad2d (float) – GSs with image plane gradient above this value will be split/duplicated. Default is 0.0002.
grow_scale3d (float) – GSs with 3D scale (normalized by scene_scale) below this value will be duplicated. Above will be split. Default is 0.01.
grow_scale2d (float) – GSs with 2D scale (normalized by image resolution) above this value will be split. Default is 0.05.
prune_scale3d (float) – GSs with 3d scale (normalized by scene_scale) above this value will be pruned. Default is 0.1.
prune_scale2d (float) – GSs with 2d scale (normalized by image resolution) above this value will be pruned. Default is 0.15.
refine_scale2d_stop_iter (int) – Stop refining GSs based on 2d scale after this iteration. Default is 0. Set to a positive value to enable this feature.
refine_start_iter (int) – Start refining GSs after this iteration. Default is 500.
refine_stop_iter (int) – Stop refining GSs after this iteration. Default is 15_000.
reset_every (int) – Reset opacities every this steps. Default is 3000.
refine_every (int) – Refine GSs every this steps. Default is 100.
pause_refine_after_reset (int) – Pause refining GSs until this number of steps after reset, Default is 0 (no pause at all) and one might want to set this number to the number of images in training set.
absgrad (bool) – Use absolute gradients for GS splitting. Default is False.
revised_opacity (bool) – Whether to use revised opacity heuristic from arXiv:2404.06109 (experimental). Default is False.
verbose (bool) – Whether to print verbose information. 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”.
Notes#
If absgrad=True, it will use the absolute gradients instead of average gradients for GS duplicating & splitting, following the AbsGS paper AbsGS: Recovering Fine Details for 3D Gaussian Splatting. Which typically leads to better results but requires to set the grow_grad2d to a higher value, e.g., 0.0008. Also, the rasterization() function should be called with absgrad=True as well so that the absolute gradients are computed.
Markov Chain Monte Carlo (MCMC) strategy#
class MCMCStrategy(
cap_max: int = 1_000_000, noise_lr: float = 5e5,
refine_start_iter: int = 500, refine_stop_iter: int = 25000,
refine_every: int = 100, min_opacity: float = 0.005,
verbose: bool = False
)
Strategy that follows the paper:
3D Gaussian Splatting as Markov Chain Monte Carlo
This strategy will:
Periodically teleport GSs with low opacity to a place that has high opacity.
Periodically introduce new GSs sampled based on the opacity distribution.
Periodically perturb the GSs locations.
Parameters:
cap_max (int) – Maximum number of GSs. Default to 1_000_000.
noise_lr (float) – MCMC samping noise learning rate. Default to 5e5.
refine_start_iter (int) – Start refining GSs after this iteration. Default to 500.
refine_stop_iter (int) – Stop refining GSs after this iteration. Default to 25_000.
refine_every (int) – Refine GSs every this steps. Default to 100.
min_opacity (float) – GSs with opacity below this value will be pruned. Default to 0.005.
verbose (bool) – Whether to print verbose information. Default to False.
Utils#
Below are the basic functions that supports the rasterization.
3DGS#
spherical_harmonics#
spherical_harmonics(
degrees_to_use: int, dirs: Tensor, coeffs: Tensor, masks: Tensor | None = None
) -> Tensor
Function: Computes spherical harmonics.
Returns:
Spherical harmonics. […, 3]
quat_scale_to_covar_preci#
quat_scale_to_covar_preci(
quats: Tensor, scales: Tensor,
compute_covar: bool = True, compute_preci: bool = True, triu: bool = False
) -> Tuple[Tensor | None, Tensor | None]
Function: Converts quaternions and scales to covariance and precision matrices.
Returns:
Covariance matrices. If triu is True the returned shape is […, 6], otherwise […, 3, 3].
Precision matrices. If triu is True the returned shape is […, 6], otherwise […, 3, 3].
Return type: tuple
Parameters:
degrees_to_use – The degree to be used.
dirs – Directions. […, 3]
coeffs – Coefficients. […, K, 3]
masks – Optional boolen masks to skip some computation. […,] Default: None.
proj#
proj(
means: Tensor, covars: Tensor, Ks: Tensor,
width: int, height: int,
camera_model: Literal["pinhole","ortho","fisheye","ftheta"] = "pinhole"
) -> Tuple[Tensor, Tensor]
Function: Projection of Gaussians (perspective or orthographic).
Returns:
Projected means. […, C, N, 2]
Projected covariances. […, C, N, 2, 2]
Return type: A tuple
Parameters:
means – Gaussian means. […, C, N, 3]
covars – Gaussian covariances. […, C, N, 3, 3]
Ks – Camera intrinsics. […, C, 3, 3]
width – Image width.
height – Image height.
world_to_cam#
world_to_cam(means: Tensor, covars: Tensor, viewmats: Tensor) -> Tuple[Tensor, Tensor]
Function: Transforms Gaussians from world to camera coordinate system.
Returns:
Gaussian means in camera coordinate system. […, C, N, 3]
Gaussian covariances in camera coordinate system. […, C, N, 3, 3]
Return type: tuple
Parameters:
means – Gaussian means. […, N, 3]
covars – Gaussian covariances. […, N, 3, 3]
viewmats – World-to-camera transformation matrices. […, C, 4, 4]
fully_fused_projection#
fully_fused_projection(
means: Tensor, covars: Tensor | None, quats: Tensor | None, scales: Tensor | None,
viewmats: Tensor, Ks: Tensor, width: int, height: int,
eps2d: float = 0.3, near_plane: float = 0.01, far_plane: float = 1e10,
radius_clip: float = 0.0, packed: bool = False, sparse_grad: bool = False,
calc_compensations: bool = False,
camera_model: Literal["pinhole","ortho","fisheye","ftheta"] = "pinhole",
opacities: Tensor | None = None
) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]
Function: Projects Gaussians to 2D. This function fuse the process of computing covariances (quat_scale_to_covar_preci()), transforming to camera space (world_to_cam()), and projection (proj()).
Returns:
If packed is True:
batch_ids. The batch indices of the projected Gaussians. Int32 tensor of shape [nnz].
camera_ids. The camera indices of the projected Gaussians. Int32 tensor of shape [nnz].
gaussian_ids. The column indices of the projected Gaussians. Int32 tensor of shape [nnz].
radii. The maximum radius of the projected Gaussians in pixel unit. Int32 tensor of shape [nnz, 2].
means. Projected Gaussian means in 2D. [nnz, 2]
depths. The z-depth of the projected Gaussians. [nnz]
conics. Inverse of the projected covariances. Return the flattend upper triangle with [nnz, 3]
compensations. The view-dependent opacity compensation factor. [nnz]
If packed is False:
radii. The maximum radius of the projected Gaussians in pixel unit. Int32 tensor of shape […, C, N, 2].
means. Projected Gaussian means in 2D. […, C, N, 2]
depths. The z-depth of the projected Gaussians. […, C, N]
conics. Inverse of the projected covariances. Return the flattend upper triangle with […, C, N, 3]
compensations. The view-dependent opacity compensation factor. […, C, N]
Return type: tuple
Parameters:
means2d – Projected Gaussian means. […, N, 2] if packed is False, [nnz, 2] if packed is True.
conics – Inverse of the projected covariances with only upper triangle values. […, N, 3] if packed is False, [nnz, 3] if packed is True.
colors – Gaussian colors or ND features. […, 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.
image_width – Image width.
image_height – Image height.
tile_size – Tile size.
isect_offsets – Intersection offsets outputs from isect_offset_encode(). […, tile_height, tile_width]
flatten_ids – The global flatten indices in [I * N] or [nnz] from isect_tiles(). [n_isects]
backgrounds – Background colors. […, channels]. Default: None.
masks – Optional tile mask to skip rendering GS to masked tiles. […, tile_height, tile_width]. Default: None.
packed – If True, the input tensors are expected to be packed with shape [nnz, …]. Default: False.
absgrad – If True, the backward pass will compute a .absgrad attribute for means2d. Default: False.
Notes:#
During projection, the Gaussians that are outside of the camera frustum are ignored. So not all the elements in the output tensors are valid. The output radii could serve as an indicator, in which zero radii means the corresponding elements are invalid in the output tensors and will be ignored in the next rasterization process. If packed=True, the output tensors will be packed into a flattened tensor, in which all elements are valid. In this case, a
batch_idstensor andcamera_idstensor will be returned to indicate the batch, camera and gaussian indices of the packed flattened tensor, which is essentially following the COO sparse tensor format.This functions supports projecting Gaussians with either covariances or {quaternions, scales}, which will be converted to covariances internally in a fused CUDA kernel. Either covars or {quats, scales} should be provided.
isect_tiles#
isect_tiles(
means2d: Tensor, radii: Tensor, depths: Tensor,
tile_size: int, tile_width: int, tile_height: int,
sort: bool = True, segmented: bool = False, packed: bool = False,
n_images: int | None = None, image_ids: Tensor | None = None, gaussian_ids: Tensor | None = None
) -> Tuple[Tensor, Tensor, Tensor]
Function:Maps projected Gaussians to intersecting tiles.
Returns:
Tiles per Gaussian. The number of tiles intersected by each Gaussian. Int32 […, N] if packed is False, Int32 [nnz] if packed is True.
Intersection ids. Each id is an 64-bit integer with the following information: image_id (Xc bits) | tile_id (Xt bits) | depth (32 bits). Xc and Xt are the maximum number of bits required to represent the image and tile ids, respectively. Int64 [n_isects]
Flatten ids. The global flatten indices in [I * N] or [nnz] (packed). [n_isects]
Return type: tuple
Parameters:
means2d – Projected Gaussian means. […, N, 2] if packed is False, [nnz, 2] if packed is True.
radii – Maximum radii of the projected Gaussians. […, N, 2] if packed is False, [nnz, 2] if packed is True.
depths – Z-depth of the projected Gaussians. […, N] if packed is False, [nnz] if packed is True.
tile_size – Tile size.
tile_width – Tile width.
tile_height – Tile height.
sort – If True, the returned intersections will be sorted by the intersection ids. Default: True.
segmented – If True, segmented radix sort will be used to sort the intersections. Default: False.
packed – If True, the input tensors are packed. Default: False.
n_images – Number of images. Required if packed is True.
image_ids – The image indices of the projected Gaussians. Required if packed is True.
gaussian_ids – The column indices of the projected Gaussians. Required if packed is True.
isect_offset_encode#
isect_offset_encode(isect_ids: Tensor, n_images: int, tile_width: int, tile_height: int) -> Tensor
Function: Encodes intersection ids to offsets.
Returns: Offsets. [I, tile_height, tile_width]
Parameters:
isect_ids – Intersection ids. [n_isects]
n_images – Number of images.
tile_width – Tile width.
tile_height – Tile height.
rasterize_to_pixels#
rasterize_to_pixels(
means2d: Tensor, conics: Tensor, colors: Tensor, opacities: Tensor,
image_width: int, image_height: int, tile_size: int,
isect_offsets: Tensor, flatten_ids: Tensor,
backgrounds: Tensor | None = None, masks: Tensor | None = None,
packed: bool = False, absgrad: bool = False
) -> Tuple[Tensor, Tensor]
Function: Rasterizes Gaussians to pixels.
Returns:
Rendered colors. […, image_height, image_width, channels]
Rendered alphas. […, image_height, image_width, 1]
Return type: tuple
Parameters:
means2d – Projected Gaussian means. […, N, 2] if packed is False, [nnz, 2] if packed is True.
conics – Inverse of the projected covariances with only upper triangle values. […, N, 3] if packed is False, [nnz, 3] if packed is True.
colors – Gaussian colors or ND features. […, 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.
image_width – Image width.
image_height – Image height.
tile_size – Tile size.
isect_offsets – Intersection offsets outputs from isect_offset_encode(). […, tile_height, tile_width]
flatten_ids – The global flatten indices in [I * N] or [nnz] from isect_tiles(). [n_isects]
backgrounds – Background colors. […, channels]. Default: None.
masks – Optional tile mask to skip rendering GS to masked tiles. […, tile_height, tile_width]. Default: None.
packed – If True, the input tensors are expected to be packed with shape [nnz, …]. Default: False.
absgrad – If True, the backward pass will compute a .absgrad attribute for means2d. Default: False.
rasterize_to_indices_in_range#
rasterize_to_indices_in_range(
range_start: int, range_end: int, transmittances: Tensor,
means2d: Tensor, conics: Tensor, opacities: Tensor,
image_width: int, image_height: int, tile_size: int,
isect_offsets: Tensor, flatten_ids: Tensor
) -> Tuple[Tensor, Tensor, Tensor]
Function: Rasterizes a batch of Gaussians to images but only returns the indices.
Returns:
Gaussian ids. Gaussian ids for the pixel intersection. A flattened list of shape [M].
Pixel ids. pixel indices (row-major). A flattened list of shape [M].
Image ids. image indices. A flattened list of shape [M].
Return type: tuple
Parameters:
range_start – The start batch of Gaussians to be rasterized (inclusive).
range_end – The end batch of Gaussians to be rasterized (exclusive).
transmittances – Currently transmittances. […, image_height, image_width]
means2d – Projected Gaussian means. […, N, 2]
conics – Inverse of the projected covariances with only upper triangle values. […, N, 3]
opacities – Gaussian opacities that support per-view values. […, N]
image_width – Image width.
image_height – Image height.
tile_size – Tile size.
isect_offsets – Intersection offsets outputs from isect_offset_encode(). […, tile_height, tile_width]
flatten_ids – The global flatten indices in [I * N] from isect_tiles(). [n_isects]
Notes#
This function supports iterative rasterization, in which each call of this function will rasterize a batch of Gaussians from near to far, defined by [range_start, range_end). If a one-step full rasterization is desired, set range_start to 0 and range_end to a really large number, e.g, 1e10.
accumulate#
accumulate(
means2d: Tensor, conics: Tensor, opacities: Tensor, colors: Tensor,
gaussian_ids: Tensor, pixel_ids: Tensor, image_ids: Tensor,
image_width: int, image_height: int
) -> Tuple[Tensor, Tensor]
Function: Alpah compositing of 2D Gaussians in Pure Pytorch. This function performs alpha compositing for Gaussians based on the pair of indices {gaussian_ids, pixel_ids, image_ids}, which annotates the intersection between all pixels and Gaussians. These intersections can be accquired from gsplat.rasterize_to_indices_in_range.
Returns:
renders: Accumulated colors. […, image_height, image_width, channels]
alphas: Accumulated opacities. […, image_height, image_width, 1]
Return type: tuple
Parameters:
means2d – Gaussian means in 2D. […, N, 2]
conics – Inverse of the 2D Gaussian covariance, Only upper triangle values. […, N, 3]
opacities – Per-view Gaussian opacities (for example, when antialiasing is enabled, Gaussian in each view would efficiently have different opacity). […, N]
colors – Per-view Gaussian colors. Supports N-D features. […, N, channels]
gaussian_ids – Collection of Gaussian indices to be rasterized. A flattened list of shape [M].
pixel_ids – Collection of pixel indices (row-major) to be rasterized. A flattened list of shape [M].
image_ids – Collection of image indices to be rasterized. A flattened list of shape [M].
image_width – Image width.
image_height – Image height.
Notes#
This function exposes the alpha compositing process into pure Pytorch. So it relies on Pytorch’s autograd for the backpropagation. It is much slower than our fully fused rasterization implementation and comsumes much more GPU memory. But it could serve as a playground for new ideas or debugging, as no backward implementation is needed.
This function requires the nerfacc package to be installed.
rasterization_inria_wrapper#
rasterization_inria_wrapper(
means: Tensor, quats: Tensor, scales:
Tensor, opacities: Tensor, colors: Tensor, viewmats: Tensor, Ks: Tensor,
width: int, height: int, near_plane: float = 0.01, far_plane: float =
100.0, eps2d: float = 0.3, sh_degree: int \| None = None, backgrounds:
Tensor \| None = None, \**kwargs
) → Tuple[Tensor, Tensor, Dict][source]
Function: Wrapper for Inria’s rasterization backend. This function exists for comparison purpose only. Only rendered image is returned.
2DGS#
fully_fused_projection_2dgs#
fully_fused_projection_2dgs(
means: Tensor, quats: Tensor, scales: Tensor,
viewmats: Tensor, Ks: Tensor, width: int, height: int,
eps2d: float = 0.3, near_plane: float = 0.01, far_plane: float = 1e10,
radius_clip: float = 0.0, packed: bool = False, sparse_grad: bool = False
) -> Tuple[Tensor, Tensor, Tensor, Tensor]
Function: Prepare Gaussians for rasterization. This function prepares ray-splat intersection matrices, computes per splat bounding box and 2D means in image space.
Returns:
If packed is True:
batch_ids. The batch indices of the projected Gaussians. Int32 tensor of shape [nnz].
camera_ids. The camera indices of the projected Gaussians. Int32 tensor of shape [nnz].
gaussian_ids. The column indices of the projected Gaussians. Int32 tensor of shape [nnz].
radii. The maximum radius of the projected Gaussians in pixel unit. Int32 tensor of shape [nnz, 2].
means. Projected Gaussian means in 2D. [nnz, 2]
depths. The z-depth of the projected Gaussians. [nnz]
ray_transforms. transformation matrices that transforms xy-planes in pixel spaces into splat coordinates (WH)^T in equation (9) in paper [nnz, 3, 3]
normals. The normals in camera spaces. [nnz, 3]
If packed is False:
radii. The maximum radius of the projected Gaussians in pixel unit. Int32 tensor of shape […, C, N, 2].
means. Projected Gaussian means in 2D. […, C, N, 2]
depths. The z-depth of the projected Gaussians. […, C, N]
ray_transforms. transformation matrices that transforms xy-planes in pixel spaces into splat coordinates […, C, N, 3, 3]
normals. The normals in camera spaces. […, C, N, 3]
Return type: tuple
Parameters:
means – Gaussian means. […, N, 3]
quats – Quaternions (No need to be normalized). […, N, 4].
scales – Scales. […, N, 3].
viewmats – World-to-camera matrices. […, C, 4, 4]
Ks – Camera intrinsics. […, C, 3, 3]
width – Image width.
height – Image height.
near_plane – Near plane distance. Default: 0.01.
far_plane – Far plane distance. Default: 200.
radius_clip – Gaussians with projected radii smaller than this value will be ignored. Default: 0.0.
packed – If True, the output tensors will be packed into a flattened tensor. Default: False.
sparse_grad (Experimental) – This is only effective when packed is True. If True, during backward the gradients of {means, covars, quats, scales} will be a sparse Tensor in COO layout. Default: False.
rasterize_to_pixels_2dgs#
rasterize_to_pixels_2dgs(
means2d: Tensor, ray_transforms: Tensor, colors: Tensor, opacities: Tensor,
normals: Tensor, densify: Tensor, image_width: int, image_height: int,
tile_size: int, isect_offsets: Tensor, flatten_ids: Tensor,
backgrounds: Tensor | None = None, masks: Tensor | None = None,
packed: bool = False, absgrad: bool = False, distloss: bool = False
) -> Tuple[Tensor, Tensor]
Function: Rasterize Gaussians to pixels.
Returns:
Rendered colors. […, image_height, image_width, channels]
Rendered alphas. […, image_height, image_width, 1]
Rendered normals. […, image_height, image_width, 3]
Rendered distortion. […, image_height, image_width, 1]
Rendered median depth.[…, image_height, image_width, 1]
Return type: tuple
Parameters:
means2d – Projected Gaussian means. […, N, 2] if packed is False, [nnz, 2] if packed is True.
ray_transforms – transformation matrices that transforms xy-planes in pixel spaces into splat coordinates. […, N, 3, 3] if packed is False, [nnz, channels] if packed is True.
colors – Gaussian colors or ND features. […, 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 normals in camera space. […, N, 3] if packed is False, [nnz, 3] if packed is True.
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 isect_offset_encode(). […, tile_height, tile_width]
flatten_ids – The global flatten indices in [I * N] or [nnz] from isect_tiles(). [n_isects]
backgrounds – Background colors. […, channels]. Default: None.
masks – Optional tile mask to skip rendering GS to masked tiles. […, tile_height, tile_width]. Default: None.
packed – If True, the input tensors are expected to be packed with shape [nnz, …]. Default: False.
absgrad – If True, the backward pass will compute a .absgrad attribute for means2d. Default: False.
rasterize_to_indices_in_range_2dgs#
rasterize_to_indices_in_range_2dgs(
range_start: int, range_end: int, transmittances: Tensor,
means2d: Tensor, ray_transforms: Tensor, opacities: Tensor,
image_width: int, image_height: int, tile_size: int,
isect_offsets: Tensor, flatten_ids: Tensor
) -> Tuple[Tensor, Tensor, Tensor]
Function: Rasterizes a batch of Gaussians to images but only returns the indices. This function supports iterative rasterization, in which each call of this function will rasterize a batch of Gaussians from near to far, defined by [range_start, range_end). If a one-step full rasterization is desired, set range_start to 0 and range_end to a really large number, e.g, 1e10.
Returns:
Gaussian ids. Gaussian ids for the pixel intersection. A flattened list of shape [M].
Pixel ids. pixel indices (row-major). A flattened list of shape [M].
Camera ids. Camera indices. A flattened list of shape [M].
Batch ids. Batch indices. A flattened list of shape [M].
Return type: tuple
Parameters:
range_start – The start batch of Gaussians to be rasterized (inclusive).
range_end – The end batch of Gaussians to be rasterized (exclusive).
transmittances – Currently transmittances. […, image_height, image_width]
means2d – Projected Gaussian means. […, N, 2]
ray_transforms – transformation matrices that transforms xy-planes in pixel spaces into splat coordinates. […, N, 3, 3]
opacities – Gaussian opacities that support per-view values. […, N]
image_width – Image width.
image_height – Image height.
tile_size – Tile size.
isect_offsets – Intersection offsets outputs from isect_offset_encode(). […, tile_height, tile_width]
flatten_ids – The global flatten indices in [I * N] from isect_tiles(). [n_isects]
accumulate_2dgs#
accumulate_2dgs(
means2d: Tensor, ray_transforms: Tensor, opacities:
Tensor, colors: Tensor, normals: Tensor, gaussian_ids: Tensor,
pixel_ids: Tensor, image_ids: Tensor, image_width: int, image_height: int
) → Tuple[Tensor, Tensor, Tensor][source]
Function: Alpha compositing for 2DGS.
Returns:
renders: Accumulated colors. […, image_height, image_width, channels]
alphas: Accumulated opacities. […, image_height, image_width, 1]
normals: Accumulated normals. […, image_height, image_width, 3]
Return type: tuple
Parameters:
means2d – Gaussian means in 2D. [C, N, 2]
ray_transforms – transformation matrices that transform rays in pixel space into splat’s local frame. [C, N, 3, 3]
opacities – Per-view Gaussian opacities (for example, when antialiasing is enabled, Gaussian in each view would efficiently have different opacity). [C, N]
colors – Per-view Gaussian colors. Supports N-D features. [C, N, channels]
normals – Per-view Gaussian normals. [C, N, 3]
gaussian_ids – Collection of Gaussian indices to be rasterized. A flattened list of shape [M].
pixel_ids – Collection of pixel indices (row-major) to be rasterized. A flattened list of shape [M].
image_ids – Collection of image indices to be rasterized. A flattened list of shape [M].
image_width – Image width.
image_height – Image height.
Notes#
This function requires the Nerfacc package to be installed.
rasterization_2dgs_inria_wrapper#
rasterization_2dgs_inria_wrapper(
means: Tensor, quats: Tensor, scales:
Tensor, opacities: Tensor, colors: Tensor, viewmats: Tensor, Ks: Tensor,
width: int, height: int, near_plane: float = 0.01, far_plane: float =
100.0, eps2d: float = 0.3, sh_degree: int \| None = None, backgrounds:
Tensor \| None = None, depth_ratio: int = 0, \**kwargs
) → Tuple[Tuple, Dict]
Function: Wrapper for 2DGS’s rasterization backend which is based on Inria’s backend.
Compression#
Compression of gaussian parameters is not supported in this version.