nerfbaselines

class nerfbaselines.Cameras(*args, **kwargs)

Bases: GenericCameras[ndarray], Protocol

class nerfbaselines.Indices(steps)

Bases: object

classmethod every_iters(iters: int, zero: bool = False)

class nerfbaselines.Method(*, checkpoint: str | None = None, train_dataset: Dataset | None = None, config_overrides: Dict[str, Any] | None = None)

Bases: Protocol

abstract get_info() → ModelInfo

Get method defaults for the trainer.

Returns:

Method info.

abstract classmethod get_method_info() → MethodInfo

Get method info needed to initialize the datasets.

Returns:

Method info.

abstract get_train_embedding(index: int) → ndarray | None

Get the embedding for the given image index.

Parameters:

index – Image index.

Returns:

Image embedding.

classmethod install()

Install the method.

abstract optimize_embeddings(dataset: Dataset, embeddings: Sequence[ndarray] | None = None) → Iterable[OptimizeEmbeddingsOutput]

Optimize embeddings for each image in the dataset.

Parameters:

• dataset – Dataset.

• embeddings – Optional initial embeddings.

abstract render(cameras: Cameras, embeddings: Sequence[ndarray] | None = None) → Iterable[RenderOutput]

Render images.

Parameters:

• cameras – Cameras.

• embeddings – Optional image embeddings.

abstract save(path: str)

Save model.

Parameters:

path – Path to save.

abstract train_iteration(step: int)

Train one iteration.

Parameters:

step – Current step.

class nerfbaselines.MethodInfo

Bases: TypedDict

name: Required[str]

required_features: FrozenSet[Literal['color', 'points3D_xyz', 'points3D_rgb']]

supported_camera_models: FrozenSet

class nerfbaselines.ModelInfo

Bases: TypedDict

batch_size: int

eval_batch_size: int

hparams: Dict[str, Any]

loaded_checkpoint: str | None

loaded_step: int | None

name: Required[str]

num_iterations: Required[int]

required_features: FrozenSet[Literal['color', 'points3D_xyz', 'points3D_rgb']]

supported_camera_models: FrozenSet

class nerfbaselines.OptimizeEmbeddingsOutput

Bases: TypedDict

embedding: ndarray

metrics: NotRequired[Dict[str, Sequence[float]]]

render_output: RenderOutput

class nerfbaselines.RenderOutput

Bases: TypedDict

accumulation: ndarray

color: Required[ndarray]

depth: ndarray