Data Parsers#

Base Data Parser#

A set of standard datasets.

class nerfstudio.data.dataparsers.base_dataparser.DataParser(config: DataParserConfig)[source]#

A dataset.

Parameters:

config – datasetparser config containing all information needed to instantiate dataset

config#

datasetparser config containing all information needed to instantiate dataset

Type:

nerfstudio.data.dataparsers.base_dataparser.DataParserConfig

includes_time#

Does the dataset include time information in the camera poses.

Type:

bool

get_dataparser_outputs(split: str = 'train', **kwargs: Optional[Dict]) → DataparserOutputs[source]#

Returns the dataparser outputs for the given split.

Parameters:

  • split – Which dataset split to generate (train/test).

  • kwargs – kwargs for generating dataparser outputs.

Returns:

DataparserOutputs containing data for the specified dataset and split

class nerfstudio.data.dataparsers.base_dataparser.DataParserConfig(_target: ~typing.Type = <factory>, data: ~pathlib.Path = PosixPath('.'))[source]#

Basic dataset config

data: Path = PosixPath('.')#

Directory specifying location of data.

class nerfstudio.data.dataparsers.base_dataparser.DataparserOutputs(image_filenames: ~typing.List[~pathlib.Path], cameras: ~nerfstudio.cameras.cameras.Cameras, alpha_color: ~typing.Optional[~jaxtyping.Float[Tensor, '3']] = None, scene_box: ~nerfstudio.data.scene_box.SceneBox = <factory>, mask_filenames: ~typing.Optional[~typing.List[~pathlib.Path]] = None, metadata: ~typing.Dict[str, ~typing.Any] = <factory>, dataparser_transform: ~jaxtyping.Float[Tensor, '3 4'] = tensor([[1., 0., 0., 0.],         [0., 1., 0., 0.],         [0., 0., 1., 0.]]), dataparser_scale: float = 1.0)[source]#

Dataparser outputs for the which will be used by the DataManager for creating RayBundle and RayGT objects.

alpha_color: Optional[Float[Tensor, '3']] = None#

Color of dataset background.

as_dict() → dict[source]#

Returns the dataclass as a dictionary.

cameras: Cameras#

Camera object storing collection of camera information in dataset.

dataparser_scale: float = 1.0#

Scale applied by the dataparser.

dataparser_transform: Float[Tensor, '3 4'] = tensor([[1., 0., 0., 0.],         [0., 1., 0., 0.],         [0., 0., 1., 0.]])#

Transform applied by the dataparser.

image_filenames: List[Path]#

Filenames for the images.

mask_filenames: Optional[List[Path]] = None#

Filenames for any masks that are required

metadata: Dict[str, Any]#

Dictionary of any metadata that be required for the given experiment. Will be processed by the InputDataset to create any additional tensors that may be required.

save_dataparser_transform(path: Path)[source]#

Save dataparser transform to json file. Some dataparsers will apply a transform to the poses, this method allows the transform to be saved so that it can be used in other applications.

Parameters:

path – path to save transform to

scene_box: SceneBox#

Scene box of dataset. Used to bound the scene or provide the scene scale depending on model.

transform_poses_to_original_space(poses: Float[Tensor, 'num_poses 3 4'], camera_convention: Literal['opengl', 'opencv'] = 'opencv') → Float[Tensor, 'num_poses 3 4'][source]#

Transforms the poses in the transformed space back to the original world coordinate system. :param poses: Poses in the transformed space :param camera_convention: Camera system convention used for the transformed poses

Returns:

Original poses

class nerfstudio.data.dataparsers.base_dataparser.Semantics(filenames: ~typing.List[~pathlib.Path], classes: ~typing.List[str], colors: ~torch.Tensor, mask_classes: ~typing.List[str] = <factory>)[source]#

Dataclass for semantic labels.

classes: List[str]#

class labels for data

colors: Tensor#

color mapping for classes

filenames: List[Path]#

filenames to load semantic data

mask_classes: List[str]#

classes to mask out from training for all modalities

nerfstudio.data.dataparsers.base_dataparser.transform_poses_to_original_space(poses: Float[Tensor, 'num_poses 3 4'], applied_transform: Float[Tensor, '3 4'], applied_scale: float, camera_convention: Literal['opengl', 'opencv'] = 'opencv') → Float[Tensor, 'num_poses 3 4'][source]#

Transforms the poses in the transformed space back to the original world coordinate system. :param poses: Poses in the transformed space :param applied_transform: Transform matrix applied in the data processing step :param applied_scale: Scale used in the data processing step :param camera_convention: Camera system convention used for the transformed poses

Returns:

Original poses

ARKitScenes#

Data parser for ARKitScenes dataset

class nerfstudio.data.dataparsers.arkitscenes_dataparser.ARKitScenes(config: ARKitScenesDataParserConfig, includes_time: bool = False)[source]#

Bases: DataParser

ARKitScenes DatasetParser

class nerfstudio.data.dataparsers.arkitscenes_dataparser.ARKitScenesDataParserConfig(_target: ~typing.Type = <factory>, data: ~pathlib.Path = PosixPath('data/ARKitScenes/3dod/Validation/41069021'), scale_factor: float = 1.0, scene_scale: float = 1.0, center_method: ~typing.Literal['poses', 'focus', 'none'] = 'poses', auto_scale_poses: bool = True, train_split_fraction: float = 0.9, depth_unit_scale_factor: float = 0.001)[source]#

Bases: DataParserConfig

ARKitScenes dataset config. ARKitScenes dataset (http://github.com/apple/ARKitScenes) is a large-scale 3D dataset of indoor scenes. This dataparser uses 3D detection subset of the ARKitScenes dataset.

auto_scale_poses: bool = True#

Whether to automatically scale the poses to fit in +/- 1 bounding box.

center_method: Literal['poses', 'focus', 'none'] = 'poses'#

The method to use to center the poses.

data: Path = PosixPath('data/ARKitScenes/3dod/Validation/41069021')#

Path to ARKitScenes folder with densely extracted scenes.

depth_unit_scale_factor: float = 0.001#

Scales the depth values to meters. Default value is 0.001 for a millimeter to meter conversion.

scale_factor: float = 1.0#

How much to scale the camera origins by.

scene_scale: float = 1.0#

How much to scale the region of interest by.

train_split_fraction: float = 0.9#

The fraction of images to use for training. The remaining images are for eval.

nerfstudio.data.dataparsers.arkitscenes_dataparser.traj_string_to_matrix(traj_string: str)[source]#

convert traj_string into translation and rotation matrices :param traj_string: A space-delimited file where each line represents a camera position at a particular timestamp. :param The file has seven columns: :param * Column 1: timestamp :param * Columns 2-4: rotation (axis-angle representation in radians) :param * Columns 5-7: translation (usually in meters)

Returns:

translation matrix Rt: rotation matrix

Return type:

ts

Blender#

Data parser for blender dataset

class nerfstudio.data.dataparsers.blender_dataparser.Blender(config: BlenderDataParserConfig)[source]#

Bases: DataParser

Blender Dataset Some of this code comes from https://github.com/yenchenlin/nerf-pytorch/blob/master/load_blender.py#L37.

class nerfstudio.data.dataparsers.blender_dataparser.BlenderDataParserConfig(_target: ~typing.Type = <factory>, data: ~pathlib.Path = PosixPath('data/blender/lego'), scale_factor: float = 1.0, alpha_color: str = 'white')[source]#

Bases: DataParserConfig

Blender dataset parser config

alpha_color: str = 'white'#

alpha color of background

data: Path = PosixPath('data/blender/lego')#

Directory specifying location of data.

scale_factor: float = 1.0#

How much to scale the camera origins by.

D-NeRF#

Data parser for blender dataset

class nerfstudio.data.dataparsers.dnerf_dataparser.DNeRF(config: DNeRFDataParserConfig)[source]#

Bases: DataParser

DNeRF Dataset

class nerfstudio.data.dataparsers.dnerf_dataparser.DNeRFDataParserConfig(_target: ~typing.Type = <factory>, data: ~pathlib.Path = PosixPath('data/dnerf/lego'), scale_factor: float = 1.0, alpha_color: str = 'white')[source]#

Bases: DataParserConfig

D-NeRF dataset parser config

alpha_color: str = 'white'#

alpha color of background

data: Path = PosixPath('data/dnerf/lego')#

Directory specifying location of data.

scale_factor: float = 1.0#

How much to scale the camera origins by.

dycheck#

Data parser for DyCheck (https://arxiv.org/abs/2210.13445) dataset of iphone subset

class nerfstudio.data.dataparsers.dycheck_dataparser.Dycheck(config: DycheckDataParserConfig)[source]#

Bases: DataParser

Dycheck (https://arxiv.org/abs/2210.13445) Dataset iphone subset

process_frames(frame_names: List[str], time_ids: ndarray) → Tuple[List, List, List][source]#

Read cameras and filenames from the name list.

Parameters:

  • frame_names – list of file names.

  • time_ids – time id of each frame.

Returns:

A list of camera, each entry is a dict of the camera.

class nerfstudio.data.dataparsers.dycheck_dataparser.DycheckDataParserConfig(_target: ~typing.Type = <factory>, data: ~pathlib.Path = PosixPath('data/iphone/mochi-high-five'), scale_factor: float = 5.0, alpha_color: str = 'white', downscale_factor: int = 1, scene_box_bound: float = 1.5)[source]#

Bases: DataParserConfig

Dycheck (https://arxiv.org/abs/2210.13445) dataset parser config

alpha_color: str = 'white'#

alpha color of background

data: Path = PosixPath('data/iphone/mochi-high-five')#

Directory specifying location of data.

downscale_factor: int = 1#

How much to downscale images.

scale_factor: float = 5.0#

How much to scale the camera origins by.

scene_box_bound: float = 1.5#

Boundary of scene box.

nerfstudio.data.dataparsers.dycheck_dataparser.downscale(img, scale: int) → ndarray[source]#

Function from DyCheck’s repo. Downscale an image.

Parameters:

  • img – Input image

  • scale – Factor of the scale

Returns:

New image

nerfstudio.data.dataparsers.dycheck_dataparser.rescale(img, scale_factor: float, interpolation: int = 3) → ndarray[source]#

Function from DyCheck’s repo. Rescale an image.

Parameters:

  • img – Input image

  • scale – Factor of the scale

  • interpolation – Interpolation method in opencv

Returns:

New image

nerfstudio.data.dataparsers.dycheck_dataparser.upscale(img, scale: int) → ndarray[source]#

Function from DyCheck’s repo. Upscale an image.

Parameters:

  • img – Input image

  • scale – Factor of the scale

Returns:

New image

Instant-NGP#

Data parser for instant ngp data

class nerfstudio.data.dataparsers.instant_ngp_dataparser.InstantNGP(config: InstantNGPDataParserConfig, includes_time: bool = False)[source]#

Bases: DataParser

Instant NGP Dataset

classmethod get_focal_lengths(meta: Dict) → Tuple[float, float][source]#

Reads or computes the focal length from transforms dict. :param meta: metadata from transforms.json file.

Returns:

Focal lengths in the x and y directions. Error is raised if these cannot be calculated.

class nerfstudio.data.dataparsers.instant_ngp_dataparser.InstantNGPDataParserConfig(_target: ~typing.Type = <factory>, data: ~pathlib.Path = PosixPath('data/ours/posterv2'), scene_scale: float = 0.3333, eval_mode: ~typing.Literal['fraction', 'filename', 'interval', 'all'] = 'fraction', train_split_fraction: float = 0.9, eval_interval: int = 8)[source]#

Bases: DataParserConfig

Instant-NGP dataset parser config

data: Path = PosixPath('data/ours/posterv2')#

Directory or explicit json file path specifying location of data.

eval_interval: int = 8#

The interval between frames to use for eval. Only used when eval_mode is eval-interval.

eval_mode: Literal['fraction', 'filename', 'interval', 'all'] = 'fraction'#

The method to use for splitting the dataset into train and eval. Fraction splits based on a percentage for train and the remaining for eval. Filename splits based on filenames containing train/eval. Interval uses every nth frame for eval. All uses all the images for any split.

scene_scale: float = 0.3333#

How much to scale the scene.

train_split_fraction: float = 0.9#

The percentage of the dataset to use for training. Only used when eval_mode is train-split-fraction.

Minimal#

Data parser for pre-prepared datasets for all cameras, with no additional processing needed Optional fields - semantics, mask_filenames, cameras.distortion_params, cameras.times

class nerfstudio.data.dataparsers.minimal_dataparser.MinimalDataParser(config: MinimalDataParserConfig, includes_time: bool = False)[source]#

Bases: DataParser

Minimal DatasetParser

class nerfstudio.data.dataparsers.minimal_dataparser.MinimalDataParserConfig(_target: ~typing.Type = <factory>, data: ~pathlib.Path = PosixPath('/home/nikhil/nerfstudio-main/tests/data/lego_test/minimal_parser'))[source]#

Bases: DataParserConfig

Minimal dataset config

data: Path = PosixPath('/home/nikhil/nerfstudio-main/tests/data/lego_test/minimal_parser')#

Directory specifying location of data.

NeRF-OSR#

Data parser for NeRF-OSR datasets

Presented in the paper: https://4dqv.mpi-inf.mpg.de/NeRF-OSR/

class nerfstudio.data.dataparsers.nerfosr_dataparser.NeRFOSR(config: NeRFOSRDataParserConfig, includes_time: bool = False)[source]#

Bases: DataParser

NeRFOSR Dataparser Presented in the paper: https://4dqv.mpi-inf.mpg.de/NeRF-OSR/

Some of this code comes from https://github.com/r00tman/NeRF-OSR/blob/main/data_loader_split.py

Source data convention is:

camera coordinate system: x–>right, y–>down, z–>scene (opencv/colmap convention) poses is camera-to-world masks are 0 for dynamic content, 255 for static content

class nerfstudio.data.dataparsers.nerfosr_dataparser.NeRFOSRDataParserConfig(_target: ~typing.Type = <factory>, data: ~pathlib.Path = PosixPath('data/NeRF-OSR/Data'), scene: str = 'stjacob', scene_scale: float = 1.0, scale_factor: float = 1.0, use_masks: bool = False, orientation_method: ~typing.Literal['pca', 'up', 'vertical', 'none'] = 'vertical', center_method: ~typing.Literal['poses', 'focus', 'none'] = 'focus', auto_scale_poses: bool = True)[source]#

Bases: DataParserConfig

Nerfstudio dataset config

auto_scale_poses: bool = True#

Whether to automatically scale the poses to fit in +/- 1 bounding box.

center_method: Literal['poses', 'focus', 'none'] = 'focus'#

The method to use for centering.

data: Path = PosixPath('data/NeRF-OSR/Data')#

Directory specifying location of data.

orientation_method: Literal['pca', 'up', 'vertical', 'none'] = 'vertical'#

The method to use for orientation.

scale_factor: float = 1.0#

How much to scale the camera origins by.

scene: str = 'stjacob'#

Which scene to load

scene_scale: float = 1.0#

How much to scale the region of interest by.

use_masks: bool = False#

Whether to use masks.

nerfstudio.data.dataparsers.nerfosr_dataparser.get_camera_params(scene_dir: str, split: Literal['train', 'validation', 'test']) → Tuple[Tensor, Tensor, int][source]#

Load camera intrinsic and extrinsic parameters for a given scene split.

Args”

scene_dir : The directory containing the scene data. split : The split for which to load the camera parameters.

Returns

A tuple containing the intrinsic parameters (as a torch.Tensor of shape [N, 4, 4]), the camera-to-world matrices (as a torch.Tensor of shape [N, 4, 4]), and the number of cameras (N).

Nerfstudio#

Data parser for nerfstudio datasets.

class nerfstudio.data.dataparsers.nerfstudio_dataparser.Nerfstudio(config: NerfstudioDataParserConfig, includes_time: bool = False, downscale_factor: Optional[int] = None)[source]#

Bases: DataParser

Nerfstudio DatasetParser

class nerfstudio.data.dataparsers.nerfstudio_dataparser.NerfstudioDataParserConfig(_target: ~typing.Type = <factory>, data: ~pathlib.Path = PosixPath('.'), scale_factor: float = 1.0, downscale_factor: ~typing.Optional[int] = None, scene_scale: float = 1.0, orientation_method: ~typing.Literal['pca', 'up', 'vertical', 'none'] = 'up', center_method: ~typing.Literal['poses', 'focus', 'none'] = 'poses', auto_scale_poses: bool = True, eval_mode: ~typing.Literal['fraction', 'filename', 'interval', 'all'] = 'fraction', train_split_fraction: float = 0.9, eval_interval: int = 8, depth_unit_scale_factor: float = 0.001)[source]#

Bases: DataParserConfig

Nerfstudio dataset config

auto_scale_poses: bool = True#

Whether to automatically scale the poses to fit in +/- 1 bounding box.

center_method: Literal['poses', 'focus', 'none'] = 'poses'#

The method to use to center the poses.

data: Path = PosixPath('.')#

Directory or explicit json file path specifying location of data.

depth_unit_scale_factor: float = 0.001#

Scales the depth values to meters. Default value is 0.001 for a millimeter to meter conversion.

downscale_factor: Optional[int] = None#

How much to downscale images. If not set, images are chosen such that the max dimension is <1600px.

eval_interval: int = 8#

The interval between frames to use for eval. Only used when eval_mode is eval-interval.

eval_mode: Literal['fraction', 'filename', 'interval', 'all'] = 'fraction'#

The method to use for splitting the dataset into train and eval. Fraction splits based on a percentage for train and the remaining for eval. Filename splits based on filenames containing train/eval. Interval uses every nth frame for eval. All uses all the images for any split.

orientation_method: Literal['pca', 'up', 'vertical', 'none'] = 'up'#

The method to use for orientation.

scale_factor: float = 1.0#

How much to scale the camera origins by.

scene_scale: float = 1.0#

How much to scale the region of interest by.

train_split_fraction: float = 0.9#

The percentage of the dataset to use for training. Only used when eval_mode is train-split-fraction.

nuScenes#

Data parser for NuScenes dataset

class nerfstudio.data.dataparsers.nuscenes_dataparser.NuScenes(config: NuScenesDataParserConfig, includes_time: bool = False)[source]#

Bases: DataParser

NuScenes DatasetParser

class nerfstudio.data.dataparsers.nuscenes_dataparser.NuScenesDataParserConfig(_target: ~typing.Type = <factory>, data: ~pathlib.Path = PosixPath('scene-0103'), data_dir: ~pathlib.Path = PosixPath('/mnt/local/NuScenes'), version: ~typing.Literal['v1.0-mini', 'v1.0-trainval'] = 'v1.0-mini', cameras: ~typing.Tuple[~typing.Literal['FRONT', 'FRONT_LEFT', 'FRONT_RIGHT', 'BACK', 'BACK_LEFT', 'BACK_RIGHT'], ...] = ('FRONT',), mask_dir: ~typing.Optional[~pathlib.Path] = None, train_split_fraction: float = 0.9, verbose: bool = False)[source]#

Bases: DataParserConfig

NuScenes dataset config. NuScenes (https://www.nuscenes.org/nuscenes) is an autonomous driving dataset containing 1000 20s clips. Each clip was recorded with a suite of sensors including 6 surround cameras. It also includes 3D cuboid annotations around objects. We optionally use these cuboids to mask dynamic objects by specifying the mask_dir flag. To create these masks use nerfstudio/scripts/datasets/process_nuscenes_masks.py.

cameras: Tuple[Literal['FRONT', 'FRONT_LEFT', 'FRONT_RIGHT', 'BACK', 'BACK_LEFT', 'BACK_RIGHT'], ...] = ('FRONT',)#

Which cameras to use.

data: Path = PosixPath('scene-0103')#

Name of the scene.

data_dir: Path = PosixPath('/mnt/local/NuScenes')#

Path to NuScenes dataset.

mask_dir: Optional[Path] = None#

Path to masks of dynamic objects.

train_split_fraction: float = 0.9#

The percent of images to use for training. The remaining images are for eval.

verbose: bool = False#

Load dataset with verbose messaging

version: Literal['v1.0-mini', 'v1.0-trainval'] = 'v1.0-mini'#

Dataset version.

nerfstudio.data.dataparsers.nuscenes_dataparser.rotation_translation_to_pose(r_quat, t_vec)[source]#

Convert quaternion rotation and translation vectors to 4x4 matrix

Phototourism#

Phototourism dataset parser. Datasets and documentation here: http://phototour.cs.washington.edu/datasets/

class nerfstudio.data.dataparsers.phototourism_dataparser.Phototourism(config: PhototourismDataParserConfig)[source]#

Bases: DataParser

Phototourism dataset. This is based on https://github.com/kwea123/nerf_pl/blob/nerfw/datasets/phototourism.py and uses colmap’s utils file to read the poses.

class nerfstudio.data.dataparsers.phototourism_dataparser.PhototourismDataParserConfig(_target: ~typing.Type = <factory>, data: ~pathlib.Path = PosixPath('data/phototourism/brandenburg-gate'), scale_factor: float = 3.0, alpha_color: str = 'white', train_split_fraction: float = 0.9, scene_scale: float = 1.0, orientation_method: ~typing.Literal['pca', 'up', 'vertical', 'none'] = 'up', center_method: ~typing.Literal['poses', 'focus', 'none'] = 'poses', auto_scale_poses: bool = True)[source]#

Bases: DataParserConfig

Phototourism dataset parser config

alpha_color: str = 'white'#

alpha color of background

auto_scale_poses: bool = True#

Whether to automatically scale the poses to fit in +/- 1 bounding box.

center_method: Literal['poses', 'focus', 'none'] = 'poses'#

The method to use to center the poses.

data: Path = PosixPath('data/phototourism/brandenburg-gate')#

Directory specifying location of data.

orientation_method: Literal['pca', 'up', 'vertical', 'none'] = 'up'#

The method to use for orientation.

scale_factor: float = 3.0#

How much to scale the camera origins by.

scene_scale: float = 1.0#

How much to scale the region of interest by.

train_split_fraction: float = 0.9#

The fraction of images to use for training. The remaining images are for eval.

ScanNet#

Data parser for ScanNet dataset

class nerfstudio.data.dataparsers.scannet_dataparser.ScanNet(config: ScanNetDataParserConfig, includes_time: bool = False)[source]#

Bases: DataParser

ScanNet DatasetParser

class nerfstudio.data.dataparsers.scannet_dataparser.ScanNetDataParserConfig(_target: ~typing.Type = <factory>, data: ~pathlib.Path = PosixPath('data/scannet/scene0423_02'), scale_factor: float = 1.0, scene_scale: float = 1.0, center_method: ~typing.Literal['poses', 'focus', 'none'] = 'poses', auto_scale_poses: bool = True, train_split_fraction: float = 0.9, depth_unit_scale_factor: float = 0.001)[source]#

Bases: DataParserConfig

ScanNet dataset config. ScanNet dataset (https://www.scan-net.org/) is a large-scale 3D dataset of indoor scenes. This dataparser assumes that the dense stream was extracted from .sens files. Expected structure of scene directory:

root/
├── color/
├── depth/
├── intrinsic/
├── pose/
auto_scale_poses: bool = True#

Whether to automatically scale the poses to fit in +/- 1 bounding box.

center_method: Literal['poses', 'focus', 'none'] = 'poses'#

The method to use to center the poses.

data: Path = PosixPath('data/scannet/scene0423_02')#

Path to ScanNet folder with densely extracted scenes.

depth_unit_scale_factor: float = 0.001#

Scales the depth values to meters. Default value is 0.001 for a millimeter to meter conversion.

scale_factor: float = 1.0#

How much to scale the camera origins by.

scene_scale: float = 1.0#

How much to scale the region of interest by.

train_split_fraction: float = 0.9#

The fraction of images to use for training. The remaining images are for eval.

SDFStudio#

Datapaser for sdfstudio formatted data

class nerfstudio.data.dataparsers.sdfstudio_dataparser.SDFStudio(config: SDFStudioDataParserConfig, includes_time: bool = False)[source]#

Bases: DataParser

SDFStudio Dataset

class nerfstudio.data.dataparsers.sdfstudio_dataparser.SDFStudioDataParserConfig(_target: ~typing.Type = <factory>, data: ~pathlib.Path = PosixPath('data/DTU/scan65'), include_mono_prior: bool = False, depth_unit_scale_factor: float = 0.001, include_foreground_mask: bool = False, downscale_factor: int = 1, scene_scale: float = 2.0, skip_every_for_val_split: int = 1, auto_orient: bool = True)[source]#

Bases: DataParserConfig

Scene dataset parser config

data: Path = PosixPath('data/DTU/scan65')#

Directory specifying location of data.

depth_unit_scale_factor: float = 0.001#

Scales the depth values to meters. Default value is 0.001 for a millimeter to meter conversion.

include_foreground_mask: bool = False#

whether or not to load foreground mask

include_mono_prior: bool = False#

whether or not to load monocular depth and normal

scene_scale: float = 2.0#

Sets the bounding cube to have edge length of this size. The longest dimension of the axis-aligned bbox will be scaled to this value.

skip_every_for_val_split: int = 1#

sub sampling validation images

sitcoms3D#

Data parser for sitcoms3D dataset.

The dataset is from the paper [“The One Where They Reconstructed 3D Humans and Environments in TV Shows”](https://ethanweber.me/sitcoms3D/)

class nerfstudio.data.dataparsers.sitcoms3d_dataparser.Sitcoms3D(config: Sitcoms3DDataParserConfig, includes_time: bool = False)[source]#

Bases: DataParser

Sitcoms3D Dataset

class nerfstudio.data.dataparsers.sitcoms3d_dataparser.Sitcoms3DDataParserConfig(_target: ~typing.Type = <factory>, data: ~pathlib.Path = PosixPath('data/sitcoms3d/TBBT-big_living_room'), include_semantics: bool = True, downscale_factor: int = 4, scene_scale: float = 2.0)[source]#

Bases: DataParserConfig

sitcoms3D dataset parser config

data: Path = PosixPath('data/sitcoms3d/TBBT-big_living_room')#

Directory specifying location of data.

include_semantics: bool = True#

whether or not to include loading of semantics data

scene_scale: float = 2.0#

Sets the bounding cube to have edge length of this size. The longest dimension of the Sitcoms3D axis-aligned bbox will be scaled to this value.