rename to splatfacto

docs/nerfology/methods/splat.md

@@ -27,11 +27,11 @@ Because gaussian splatting trains on *full images* instead of bundles of rays, t
 
 
 ### Running the Method
-To run gaussian splatting, run `ns-train gaussian-splatting --data <data>`. Just like NeRF methods, the splat can be interactively viewed in the web-viewer, loaded from a checkpoint, rendered, and exported.
+To run gaussian splatting, run `ns-train splatfacto --data <data>`. Just like NeRF methods, the splat can be interactively viewed in the web-viewer, loaded from a checkpoint, rendered, and exported.
 
 #### Quality and Regularization
 The default settings provided maintain a balance between speed, quality, and splat file size, but if you care more about quality than training speed or size, you can decrease the alpha cull threshold 
-(threshold to delete translucent gaussians) and disable culling after 15k steps like so: `ns-train gaussian-splatting --pipeline.model.cull_scale_thresh=0.005 --pipeline.model.continue_cull_post_densification=False --data <data>`
+(threshold to delete translucent gaussians) and disable culling after 15k steps like so: `ns-train splatfacto --pipeline.model.cull_scale_thresh=0.005 --pipeline.model.continue_cull_post_densification=False --data <data>`
 
 A common artifact in splatting is long, spikey gaussians. [PhysGaussian](https://xpandora.github.io/PhysGaussian/) proposes an example of a scale-regularizer that encourages gaussians to be more evenly shaped. To enable this, set the `use_scale_regularization` flag to `True`.
 

nerfstudio/configs/method_configs.py

@@ -51,14 +51,14 @@
 from nerfstudio.field_components.temporal_distortions import TemporalDistortionKind
 from nerfstudio.fields.sdf_field import SDFFieldConfig
 from nerfstudio.models.depth_nerfacto import DepthNerfactoModelConfig
-from nerfstudio.models.gaussian_splatting import GaussianSplattingModelConfig
 from nerfstudio.models.generfacto import GenerfactoModelConfig
 from nerfstudio.models.instant_ngp import InstantNGPModelConfig
 from nerfstudio.models.mipnerf import MipNerfModel
 from nerfstudio.models.nerfacto import NerfactoModelConfig
 from nerfstudio.models.neus import NeuSModelConfig
 from nerfstudio.models.neus_facto import NeuSFactoModelConfig
 from nerfstudio.models.semantic_nerfw import SemanticNerfWModelConfig
+from nerfstudio.models.splatfacto import SplatfactoModelConfig
 from nerfstudio.models.tensorf import TensoRFModelConfig
 from nerfstudio.models.vanilla_nerf import NeRFModel, VanillaModelConfig
 from nerfstudio.pipelines.base_pipeline import VanillaPipelineConfig
@@ -80,7 +80,7 @@
     "generfacto": "Generative Text to NeRF model",
     "neus": "Implementation of NeuS. (slow)",
     "neus-facto": "Implementation of NeuS-Facto. (slow)",
-    "gaussian-splatting": "Gaussian Splatting model",
+    "splatfacto": "Gaussian Splatting model",
 }
 
 method_configs["nerfacto"] = TrainerConfig(
@@ -588,8 +588,8 @@
     vis="viewer",
 )
 
-method_configs["gaussian-splatting"] = TrainerConfig(
-    method_name="gaussian-splatting",
+method_configs["splatfacto"] = TrainerConfig(
+    method_name="splatfacto",
     steps_per_eval_image=100,
     steps_per_eval_batch=0,
     steps_per_save=2000,
@@ -601,7 +601,7 @@
         datamanager=FullImageDatamanagerConfig(
             dataparser=NerfstudioDataParserConfig(load_3D_points=True),
         ),
-        model=GaussianSplattingModelConfig(),
+        model=SplatfactoModelConfig(),
     ),
     optimizers={
         "xyz": {

nerfstudio/data/dataparsers/nerfstudio_dataparser.py

@@ -386,7 +386,7 @@ def _generate_dataparser_outputs(self, split="train"):
             else:
                 if not self.prompted_user:
                     CONSOLE.print(
-                        "[bold yellow]Warning: load_3D_points set to true but no point cloud found. gaussian-splatting models will use random point cloud initialization."
+                        "[bold yellow]Warning: load_3D_points set to true but no point cloud found. splatfacto models will use random point cloud initialization."
                     )
                 ply_file_path = None
 

nerfstudio/models/gaussian_splatting.py → nerfstudio/models/splatfacto.py

@@ -99,10 +99,10 @@ def projection_matrix(znear, zfar, fovx, fovy, device: Union[str, torch.device]
 
 
 @dataclass
-class GaussianSplattingModelConfig(ModelConfig):
+class SplatfactoModelConfig(ModelConfig):
     """Gaussian Splatting Model Config"""
 
-    _target: Type = field(default_factory=lambda: GaussianSplattingModel)
+    _target: Type = field(default_factory=lambda: SplatfactoModel)
     warmup_length: int = 500
     """period of steps where refinement is turned off"""
     refine_every: int = 100
@@ -149,14 +149,14 @@ class GaussianSplattingModelConfig(ModelConfig):
     """
 
 
-class GaussianSplattingModel(Model):
+class SplatfactoModel(Model):
     """Gaussian Splatting model
 
     Args:
         config: Gaussian Splatting configuration to instantiate model
     """
 
-    config: GaussianSplattingModelConfig
+    config: SplatfactoModelConfig
 
     def __init__(
         self,

nerfstudio/scripts/exporter.py

@@ -40,7 +40,7 @@
 from nerfstudio.exporter.exporter_utils import collect_camera_poses, generate_point_cloud, get_mesh_from_filename
 from nerfstudio.exporter.marching_cubes import generate_mesh_with_multires_marching_cubes
 from nerfstudio.fields.sdf_field import SDFField  # noqa
-from nerfstudio.models.gaussian_splatting import GaussianSplattingModel
+from nerfstudio.models.splatfacto import SplatfactoModel
 from nerfstudio.pipelines.base_pipeline import Pipeline, VanillaPipeline
 from nerfstudio.utils.eval_utils import eval_setup
 from nerfstudio.utils.rich_utils import CONSOLE
@@ -488,9 +488,9 @@ def main(self) -> None:
 
         _, pipeline, _, _ = eval_setup(self.load_config)
 
-        assert isinstance(pipeline.model, GaussianSplattingModel)
+        assert isinstance(pipeline.model, SplatfactoModel)
 
-        model: GaussianSplattingModel = pipeline.model
+        model: SplatfactoModel = pipeline.model
 
         filename = self.output_dir / "splat.ply"
 

nerfstudio/viewer/export_panel.py

@@ -22,7 +22,7 @@
 
 from nerfstudio.data.scene_box import OrientedBox
 from nerfstudio.models.base_model import Model
-from nerfstudio.models.gaussian_splatting import GaussianSplattingModel
+from nerfstudio.models.splatfacto import SplatfactoModel
 from nerfstudio.viewer.control_panel import ControlPanel
 
 
@@ -32,7 +32,7 @@ def populate_export_tab(
     config_path: Path,
     viewer_model: Model,
 ) -> None:
-    viewing_gsplat = isinstance(viewer_model, GaussianSplattingModel)
+    viewing_gsplat = isinstance(viewer_model, SplatfactoModel)
     if not viewing_gsplat:
         crop_output = server.add_gui_checkbox("Use Crop", False)
 

nerfstudio/viewer/render_state_machine.py

@@ -28,7 +28,7 @@
 
 from nerfstudio.cameras.cameras import Cameras
 from nerfstudio.model_components.renderers import background_color_override_context
-from nerfstudio.models.gaussian_splatting import GaussianSplattingModel
+from nerfstudio.models.splatfacto import SplatfactoModel
 from nerfstudio.utils import colormaps, writer
 from nerfstudio.utils.writer import GLOBAL_BUFFER, EventName, TimeWriter
 from nerfstudio.viewer.utils import CameraState, get_camera
@@ -136,7 +136,7 @@ def _render_img(self, camera_state: CameraState):
 
         with TimeWriter(None, None, write=False) as vis_t:
             with self.viewer.train_lock if self.viewer.train_lock is not None else contextlib.nullcontext():
-                if isinstance(self.viewer.get_model(), GaussianSplattingModel):
+                if isinstance(self.viewer.get_model(), SplatfactoModel):
                     color = self.viewer.control_panel.background_color
                     background_color = torch.tensor(
                         [color[0] / 255.0, color[1] / 255.0, color[2] / 255.0],
@@ -168,7 +168,7 @@ def _render_img(self, camera_state: CameraState):
                 self.viewer.get_model().train()
             num_rays = (camera.height * camera.width).item()
             if self.viewer.control_panel.layer_depth:
-                if isinstance(self.viewer.get_model(), GaussianSplattingModel):
+                if isinstance(self.viewer.get_model(), SplatfactoModel):
                     # Gaussians render much faster than we can send depth images, so we do some downsampling.
                     assert len(outputs["depth"].shape) == 3
                     assert outputs["depth"].shape[-1] == 1

nerfstudio/viewer/viewer.py

@@ -32,7 +32,7 @@
 from nerfstudio.configs import base_config as cfg
 from nerfstudio.data.datasets.base_dataset import InputDataset
 from nerfstudio.models.base_model import Model
-from nerfstudio.models.gaussian_splatting import GaussianSplattingModel
+from nerfstudio.models.splatfacto import SplatfactoModel
 from nerfstudio.pipelines.base_pipeline import Pipeline
 from nerfstudio.utils.decorators import check_main_thread, decorate_all
 from nerfstudio.utils.writer import GLOBAL_BUFFER, EventName
@@ -250,7 +250,7 @@ def nested_folder_install(folder_labels: List[str], prev_labels: List[str], elem
 
         # Diagnostics for Gaussian Splatting: where the points are at the start of training.
         # This is hidden by default, it can be shown from the Viser UI's scene tree table.
-        if isinstance(pipeline.model, GaussianSplattingModel):
+        if isinstance(pipeline.model, SplatfactoModel):
             self.viser_server.add_point_cloud(
                 "/gaussian_splatting_initial_points",
                 points=pipeline.model.means.numpy(force=True) * VISER_NERFSTUDIO_SCALE_RATIO,

nerfstudio/viewer_legacy/server/render_state_machine.py

@@ -24,7 +24,7 @@
 
 from nerfstudio.cameras.cameras import Cameras
 from nerfstudio.model_components.renderers import background_color_override_context
-from nerfstudio.models.gaussian_splatting import GaussianSplattingModel
+from nerfstudio.models.splatfacto import SplatfactoModel
 from nerfstudio.utils import colormaps, writer
 from nerfstudio.utils.writer import GLOBAL_BUFFER, EventName, TimeWriter
 from nerfstudio.viewer_legacy.server import viewer_utils
@@ -130,7 +130,7 @@ def _render_img(self, cam_msg: CameraMessage):
         with self.viewer.train_lock if self.viewer.train_lock is not None else contextlib.nullcontext():
             # TODO jake-austin: Make this check whether the model inherits from a camera based model or a ray based model
             # TODO Zhuoyang: First made some dummy judgements, need to be fixed later
-            isGaussianSplattingModel = isinstance(self.viewer.get_model(), GaussianSplattingModel)
+            isGaussianSplattingModel = isinstance(self.viewer.get_model(), SplatfactoModel)
             if isGaussianSplattingModel:
                 # TODO fix me before ship
                 camera_ray_bundle = camera.generate_rays(camera_indices=0, aabb_box=self.viewer.get_model().render_aabb)

tests/test_train.py

@@ -26,7 +26,7 @@
     "neus",
     "generfacto",
     "neus-facto",
-    "gaussian-splatting",
+    "splatfacto",
 ]