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Functions505 in github.com/Anttwo/MILo

⨍ Functions 505◇ Types & classes 80

↓ 1 callersFunctiontransformPoint4x4

submodules/diff-gaussian-rasterization_ms/cuda_rasterizer/auxiliary.h:86

↓ 1 callersFunctiontransformPoint4x4

submodules/diff-gaussian-rasterization/cuda_rasterizer/auxiliary.h:84

↓ 1 callersFunctiontransformPoint4x4

submodules/diff-gaussian-rasterization_gof/cuda_rasterizer/auxiliary.h:106

↓ 1 callersFunctiontransform_points_to_pixel_space

Transform points from world space (3 coordinates) to pixel space (2 coordinates). Args: points (torch.Tensor): Should have shape

milo/regularization/sdf/depth_fusion.py:37

↓ 1 callersFunctiontransform_points_world_to_view

Transform points from world space to view space. Args: points (torch.Tensor): Should have shape (n_cameras, N, 3). cameras (List[

milo/regularization/sdf/depth_fusion.py:12

↓ 1 callersFunctiontriangulate

submodules/tetra_triangulation/src/triangulation.cpp:28

↓ 1 callersFunctionupdate_gaussian_parameters

Return the new Gaussian parameters after applying a 3D affine transform. Args: means (torch.Tensor): (..., 3) scales (torch.Tenso

milo/blender/editing.py:105

↓ 1 callersMethodz_score

(self, x: torch.Tensor)

milo/lpipsPyTorch/modules/networks.py:50

MethodException

submodules/tetra_triangulation/src/utils/exception.h:40

FunctionPYBIND11_MODULE

submodules/diff-gaussian-rasterization_ms/ext.cpp:15

FunctionPYBIND11_MODULE

submodules/tetra_triangulation/src/py_binding.cpp:46

FunctionPYBIND11_MODULE

submodules/fused-ssim/ext.cpp:4

FunctionPYBIND11_MODULE

submodules/diff-gaussian-rasterization/ext.cpp:15

FunctionPYBIND11_MODULE

submodules/simple-knn/ext.cpp:15

FunctionPYBIND11_MODULE

submodules/diff-gaussian-rasterization_gof/ext.cpp:15

FunctionPix2ndc

submodules/diff-gaussian-rasterization_ms/cuda_rasterizer/auxiliary.h:46

FunctionSHRender

(xyz_sampled, viewdirs, features, sh_degree=2)

milo/eval/mesh_nvs/tensorf_utils.py:98

(self, mesh_path, estimate_c2w_list)

milo/eval/tnt/cull_mesh.py:292

( self, sh_degree : int, use_mip_filter : bool = False, learn_occupancy : bool = Fal

milo/scene/gaussian_model.py:70

(self, num_input_channels, num_output_channels)

milo/scene/appearance_network.py:19

b :param path: Path to colmap scene main folder.

milo/scene/__init__.py:25

(self, colmap_id, R, T, FoVx, FoVy, image, gt_alpha_mask, image_name, uid, t

milo/scene/cameras.py:18

(self, width, height, fovy, fovx, znear, zfar, world_view_transform, full_proj_transform)

milo/scene/cameras.py:64

( self, image_size=(1080, 1920), blur_radius=0.0, faces_per_pixel=1,

milo/scene/mesh.py:146

(self, bary_coords, zbuf, pix_to_face)

milo/scene/mesh.py:158

( self, cameras:Union[List[Camera], Camera]=None, raster_settings:RasterizationSettin

milo/scene/mesh.py:168

(self, rasterizer:MeshRasterizer)

milo/scene/mesh.py:257

(self, rasterizer:MeshRasterizer)

milo/scene/mesh.py:375

A class for computing a TSDF field from a set of points and a collection of posed depth maps. Args: points (torc

milo/regularization/sdf/depth_fusion.py:218

a class that extracts attributes a scene presented by 2DGS Usage example: >>> gaussExtrator = GaussianExtractor(gaussians, r

milo/utils/regular_tsdf_utils.py:80

milo/utils/general_utils.py:115

(self, meta, mat)

milo/eval/tnt/trajectory_io.py:7

Mesher class, given a scene representation, the mesher extracts the mesh from it. Args: cfg: (dict

milo/eval/tnt/cull_mesh.py:70

(self,inChanel, feape=6, featureC=128, output_dim=3)

milo/eval/mesh_nvs/tensorf_utils.py:111

(self,inChanel, feape=6, featureC=128, output_dim=3)

milo/eval/mesh_nvs/tensorf_utils.py:244

(self,inChanel, viewpe=6, feape=6, featureC=128)

milo/eval/mesh_nvs/tensorf_utils.py:275

(self, n_voxels=3000**3, device='cuda', use_mlp=False, app_dim=27, app_n_comp=16, **kargs)

milo/eval/mesh_nvs/mesh_nvs_utils.py:220

(self, mesh : Meshes, color_field_type : str = "ColorFieldVM", scene_extent : float = 1.0)

milo/eval/mesh_nvs/mesh_nvs_utils.py:281

(self, parser, sentinel=False)

milo/arguments/__init__.py:48

milo/arguments/__init__.py:68

milo/arguments/__init__.py:75

milo/blender/editing.py:424

(self, n_channels_list: Sequence[int])

milo/lpipsPyTorch/modules/networks.py:24

milo/lpipsPyTorch/modules/networks.py:37

milo/lpipsPyTorch/modules/networks.py:67

milo/lpipsPyTorch/modules/networks.py:78

(self, net_type: str = 'alex', version: str = '0.1')

milo/lpipsPyTorch/modules/lpips.py:17

(self, params, lr, eps)

submodules/diff-gaussian-rasterization_ms/diff_gaussian_rasterization_ms/__init__.py:498

(self, name: str, sourcedir: str = "")

submodules/tetra_triangulation/setup.py:23

(self, raster_settings)

submodules/diff-gaussian-rasterization/diff_gaussian_rasterization/__init__.py:189

(self, raster_settings)

submodules/diff-gaussian-rasterization_gof/diff_gaussian_rasterization_gof/__init__.py:184

milo/eval/tnt/trajectory_io.py:11

Function_evaluate_sdf_values

Evaluate truncated SDF values at the given pivots using Depth Fusion. Args: pivots (torch.Tensor): Points at which to compute the SDF val

milo/functional/sdf.py:13

Methodaggressive_clone

(self, scene, render_simp, iteration, args, pipe, background)

milo/scene/gaussian_model.py:1238

(ctx, grad_out_color, *_)

submodules/diff-gaussian-rasterization_ms/diff_gaussian_rasterization_ms/__init__.py:283

(ctx, grad_color, grad_radii, grad_coord, grad_mcoord, grad_depth, grad_mdepth, grad_alpha,grad_normal)

submodules/diff-gaussian-rasterization/diff_gaussian_rasterization/__init__.py:104

(ctx, grad_out_color, _)

submodules/diff-gaussian-rasterization_gof/diff_gaussian_rasterization_gof/__init__.py:107

Methodbuild_covariance_from_scaling_rotation

(scaling, scaling_modifier, rotation)

milo/scene/gaussian_model.py:53

Functioncenter_poses

Center the poses so that we can use NDC. See https://github.com/bmild/nerf/issues/34 Inputs: poses: (N_images, 3, 4) Outputs:

milo/utils/video_utils.py:71

Functioncombine_meshes

Combines multiple meshes into a single mesh. Args: meshes (List[Meshes]): List of meshes to combine. Returns: Meshes: Combin

milo/scene/mesh.py:115

Functioncompute_adjacency_from_tets

(delaunay_tets: torch.Tensor, num_points: int, chunk_size: int = 5_000_000, index_dtype: torch.dtype = torch.l

milo/utils/adjacency_utils.py:53

Functioncompute_adjacency_from_tets_no_chunking

Computes the adjacency information from Delaunay tetrahedra. This function processes a set of Delaunay tetrahedra to compute the adjacency

milo/utils/adjacency_utils.py:6

Methodcompute_appfeature

(self, xyz_sampled)

milo/eval/mesh_nvs/mesh_nvs_utils.py:212

Functioncompute_delaunay_triangulation

Compute Delaunay tetrahedralization for a set of Gaussian pivots. Override pivots can be provided; otherwise, pivots will be extracted from Gaussi

milo/functional/delaunay.py:8

Functioncompute_initial_sdf_values

Compute initial SDF values for a set of Gaussians, or precomputed pivots. Args: views (List[Camera]): List of cameras. render_fun

milo/functional/sdf.py:83

Methoddefine_modules

milo/eval/mesh_nvs/mesh_nvs_utils.py:200

Methoddensify_and_prune

(self, max_grad, min_opacity, extent, max_screen_size)

milo/scene/gaussian_model.py:867

Functiondepth_order_weight_schedule

(iteration:int, schedule:str="default")

milo/regularization/depth/depth_order.py:4

Functiondnormvdv

submodules/diff-gaussian-rasterization_ms/cuda_rasterizer/auxiliary.h:126

Functiondnormvdv

submodules/diff-gaussian-rasterization/cuda_rasterizer/auxiliary.h:123

Functiondnormvdv

submodules/diff-gaussian-rasterization_gof/cuda_rasterizer/auxiliary.h:145

Functiondnormvdz

submodules/diff-gaussian-rasterization_ms/cuda_rasterizer/auxiliary.h:118

Functiondnormvdz

submodules/diff-gaussian-rasterization/cuda_rasterizer/auxiliary.h:115

Functiondnormvdz

submodules/diff-gaussian-rasterization_gof/cuda_rasterizer/auxiliary.h:137

(self, context)

milo/blender/milo_addon.py:508

(self, context)

milo/blender/milo_addon.py:562

submodules/diff-gaussian-rasterization/cuda_rasterizer/auxiliary.h:189

Functionevaluate_cull_sdf_values_chunk

( points:torch.Tensor, chunk_size:int, **kwargs )

milo/regularization/sdf/integration.py:102

Functionevaluate_sdf_values

( points:torch.Tensor, views:List[Camera], masks:torch.Tensor, gaussians:GaussianModel,

milo/regularization/sdf/depth_fusion.py:465

Functionevaluate_sdf_values

(points, views, gaussians, pipeline, background, kernel_size, isosurface_value=0.5, integrate_func:Callable=No

milo/regularization/sdf/integration.py:12

(self, context)

milo/blender/milo_addon.py:105

(self, context)

milo/blender/milo_addon.py:127

(self, context)

milo/blender/milo_addon.py:144

(self, context)

milo/blender/milo_addon.py:305

(self, context)

milo/blender/milo_addon.py:350

(self, context)

milo/blender/milo_addon.py:395

milo/arguments/__init__.py:62

Functionextract_mesh

Differentiably extract a mesh from Gaussian parameters, including initial or updated SDF values for the Gaussian pivots. This function is dif

milo/functional/mesh.py:32

Methodface_normals

milo/scene/mesh.py:65

Functionfast_ssim

milo/utils/loss_utils.py:85

Methodfinalize_options

Set final values for all the options that this command supports. This is always called as late as possible, ie. after any option assi

submodules/tetra_triangulation/setup.py:43

FunctionfindEigenvaluesSymReal

submodules/diff-gaussian-rasterization/cuda_rasterizer/auxiliary.h:218

milo/scene/appearance_network.py:12

milo/scene/appearance_network.py:33

( self, mesh:Meshes, cameras:List[Camera]=None, cam_idx:int=0, retur

milo/scene/mesh.py:198

( self, mesh:Meshes, cameras:Union[List[Camera], Camera]=None, cam_idx=0,

milo/scene/mesh.py:262

( self, mesh:Meshes, cameras:Union[List[Camera], Camera]=None, cam_idx:int=

milo/scene/mesh.py:380

(ctx, C1, C2, img1, img2)

milo/utils/loss_utils.py:24

(self, features)

milo/eval/mesh_nvs/tensorf_utils.py:123

(self, features, omega_r, rho, omega_o_dot_normal)

milo/eval/mesh_nvs/tensorf_utils.py:258

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