Rename gaussian-splatting to splatfacto for clarity

docs/index.md

@@ -140,7 +140,7 @@ This documentation is organized into 3 parts:
 - [NeRF](nerfology/methods/nerf.md): OG Neural Radiance Fields
 - [Mip-NeRF](nerfology/methods/mipnerf.md): A Multiscale Representation for Anti-Aliasing Neural Radiance Fields
 - [TensoRF](nerfology/methods/tensorf.md): Tensorial Radiance Fields
-- [Gaussian Splatting](nerfology/methods/splat.md): 3D Gaussian Splatting
+- [Splatfacto](nerfology/methods/splat.md): Nerfstudio's Gaussian Splatting implementation
 
 (third_party_methods)=
 

docs/nerfology/methods/index.md

@@ -27,7 +27,7 @@ The following methods are supported in nerfstudio:
 ```{toctree}
     :maxdepth: 1
     Instant-NGP<instant_ngp.md>
-    3D Gaussian Splatting<splat.md>
+    Splatfacto<splat.md>
     Instruct-NeRF2NeRF<in2n.md>
     K-Planes<kplanes.md>
     LERF<lerf.md>

docs/nerfology/methods/splat.md

@@ -1,6 +1,6 @@
-# Gaussian Splatting
-<h4>Real-Time Radiance Field Rendering</h4>
-
+# Splatfacto
+<h4>Nerfstudio's Gaussian Splatting Implementation</h4>
+<iframe width="560" height="315" src="https://www.youtube.com/embed/0yueTFx-MdQ?si=GxiYnFAeYVVl-soJ" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
 
 ```{button-link} https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/
 :color: primary
@@ -10,6 +10,8 @@ Paper Website
 
 [3D Gaussian Splatting](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/) was proposed in SIGGRAPH 2023 from INRIA, and is a completely different method of representing radiance fields by explicitly storing a collection of 3D volumetric gaussians. These can be "splatted", or projected, onto a 2D image provided a camera pose, and rasterized to obtain per-pixel colors. Because rasterization is very fast on GPUs, this method can render much faster than neural representations of radiance fields.
 
+To avoid confusion with the original paper, we refer to nerfstudio's implementation as "Splatfacto", which will drift away from the original as more features are added. Just as Nerfacto is a blend of various different methods, Splatfacto will be a blend of different gaussian splatting methodologies.
+
 ### Installation
 
 ```{button-link} https://docs.gsplat.studio/
@@ -18,30 +20,30 @@ Paper Website
 GSplat 
 ```
 
-Nerfstudio uses [gsplat](https://github.com/nerfstudio-project/gsplat) as its gaussian rasterization backend, an in-house re-implementation which is designed to be more developer friendly. This can be installed with `pip install gsplat`. The associated CUDA code will be compiled the first time gaussian splatting is executed. Some users with PyTorch 2.0 have experienced issues with this, which can be resolved by either installing gsplat from source, or upgrading torch to 2.1.
+Nerfstudio uses [gsplat](https://github.com/nerfstudio-project/gsplat) as its gaussian rasterization backend, an in-house re-implementation which is designed to be more developer friendly. This can be installed with `pip install gsplat`. The associated CUDA code will be compiled the first time gsplat is executed. Some users with PyTorch 2.0 have experienced issues with this, which can be resolved by either installing gsplat from source, or upgrading torch to 2.1.
 
 ### Data
-Gaussian Splatting works much better if you initialize it from pre-existing geometry, such as SfM points from COLMAP. COLMAP datasets or datasets from `ns-process-data` will automatically save these points and initialize gaussians on them. Other datasets currently do not support initialization, and will initialize gaussians randomly. Initializing from other data inputs (i.e. depth from phone app scanners) may be supported in the future.
+Gaussian spltting works much better if you initialize it from pre-existing geometry, such as SfM points from COLMAP. COLMAP datasets or datasets from `ns-process-data` will automatically save these points and initialize gaussians on them. Other datasets currently do not support initialization, and will initialize gaussians randomly. Initializing from other data inputs (i.e. depth from phone app scanners) may be supported in the future.
 
-Because gaussian splatting trains on *full images* instead of bundles of rays, there is a new datamanager in `full_images_datamanager.py` which undistorts input images, caches them, and provides single images at each train step.
+Because the method trains on *full images* instead of bundles of rays, there is a new datamanager in `full_images_datamanager.py` which undistorts input images, caches them, and provides single images at each train step.
 
 
 ### 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 splatfacto, 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`.
+A common artifact in splatting is long, spikey gaussians. [PhysGaussian](https://xpandora.github.io/PhysGaussian/) proposes a scale regularizer that encourages gaussians to be more evenly shaped. To enable this, set the `use_scale_regularization` flag to `True`.
 
 ### Details
 For more details on the method, see the [original paper](https://arxiv.org/abs/2308.04079). Additionally, for a detailed derivation of the gradients used in the gsplat library, see [here](https://arxiv.org/abs/2312.02121).
 
 ### Exporting splats
 Gaussian splats can be exported as a `.ply` file which are ingestable by a variety of online web viewers. You can do this via the viewer, or `ns-export gaussian-splat --load-config <config> --output-dir exports/splat`. Currently splats can only be exported from trained splats, not from nerfacto.
 
-Nerfstudio gaussian splat exports currently supports multiple third-party splat viewers:
+Nerfstudio's splat export currently supports multiple third-party splat viewers:
 - [Polycam Viewer](https://poly.cam/tools/gaussian-splatting)
 - [Playcanvas SuperSplat](https://playcanvas.com/super-splat)
 - [WebGL Viewer by antimatter15](https://antimatter15.com/splat/) 
@@ -50,6 +52,8 @@ Nerfstudio gaussian splat exports currently supports multiple third-party splat
 
 ### FAQ
 - Can I export a mesh or pointcloud?
-Currently these export options are not supported, but may become in the future. Contributions are always welcome!
+
+Currently these export options are not supported, but may be in the future. Contributions are always welcome!
 - Can I render fisheye, equirectangular, orthographic images?
-Currently, no. Gaussian splatting assumes a perspective camera for its rasterization pipeline. Implementing other camera models is of interest but not currently planned.
+
+Currently, no. Gaussian rasterization assumes a perspective camera for its rasterization pipeline. Implementing other camera models is of interest but not currently planned.

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 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):
-    """Gaussian Splatting Model Config"""
+class SplatfactoModelConfig(ModelConfig):
+    """Splatfacto Model Config, nerfstudio's implementation of Gaussian Splatting"""
 
-    _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):
-    """Gaussian Splatting model
+class SplatfactoModel(Model):
+    """Nerfstudio's implementation of Gaussian Splatting
 
     Args:
-        config: Gaussian Splatting configuration to instantiate model
+        config: Splatfacto 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",
 ]