# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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"""Fields for nerf-w"""
from typing import Dict, Optional, Tuple
import torch
from jaxtyping import Float
from torch import Tensor, nn
from nerfstudio.cameras.rays import RaySamples
from nerfstudio.field_components.embedding import Embedding
from nerfstudio.field_components.encodings import Encoding, Identity
from nerfstudio.field_components.field_heads import (
DensityFieldHead,
FieldHeadNames,
RGBFieldHead,
TransientDensityFieldHead,
TransientRGBFieldHead,
UncertaintyFieldHead,
)
from nerfstudio.field_components.mlp import MLP
from nerfstudio.fields.base_field import Field
[docs]class VanillaNerfWField(Field):
"""The NeRF-W field which has appearance and transient conditioning.
Args:
num_images: How many images exist in the dataset.
position_encoding: Position encoder.
direction_encoding: Direction encoder.
base_mlp_num_layers: Number of layers for base MLP.
base_mlp_layer_width: Width of base MLP layers.
head_mlp_num_layers: Number of layer for output head MLP.
head_mlp_layer_width: Width of output head MLP layers.
appearance_embedding_dim:: Dimension of appearance embedding.
transient_embedding_dim:: Dimension of transient embedding.
skip_connections: Where to add skip connection in base MLP.
"""
def __init__(
self,
num_images: int,
position_encoding: Encoding = Identity(in_dim=3),
direction_encoding: Encoding = Identity(in_dim=3),
base_mlp_num_layers: int = 8,
base_mlp_layer_width: int = 256,
head_mlp_num_layers: int = 2,
head_mlp_layer_width: int = 128,
appearance_embedding_dim: int = 48,
transient_embedding_dim: int = 16,
skip_connections: Tuple[int] = (4,),
) -> None:
super().__init__()
self.num_images = num_images
self.position_encoding = position_encoding
self.direction_encoding = direction_encoding
self.base_mlp_num_layers = base_mlp_num_layers
self.base_mlp_layer_width = base_mlp_layer_width
self.head_mlp_num_layers = head_mlp_num_layers
self.head_mlp_layer_width = head_mlp_layer_width
self.appearance_embedding_dim = appearance_embedding_dim
self.transient_embedding_dim = transient_embedding_dim
if self.appearance_embedding_dim > 0:
self.embedding_appearance = Embedding(self.num_images, self.appearance_embedding_dim)
else:
self.embedding_appearance = None
self.embedding_transient = Embedding(self.num_images, self.transient_embedding_dim)
self.mlp_base = MLP(
in_dim=self.position_encoding.get_out_dim(),
num_layers=base_mlp_num_layers,
layer_width=base_mlp_layer_width,
skip_connections=skip_connections,
out_activation=nn.ReLU(),
)
self.mlp_transient = MLP(
in_dim=self.mlp_base.get_out_dim() + self.embedding_transient.get_out_dim(),
out_dim=base_mlp_layer_width // 2,
num_layers=4,
layer_width=base_mlp_layer_width,
activation=nn.ReLU(),
out_activation=nn.ReLU(),
)
self.mlp_head = MLP(
in_dim=self.mlp_base.get_out_dim()
+ self.direction_encoding.get_out_dim()
+ (self.embedding_appearance.get_out_dim() if self.embedding_appearance is not None else 0),
num_layers=head_mlp_num_layers,
layer_width=head_mlp_layer_width,
out_activation=nn.ReLU(),
)
self.field_head_density = DensityFieldHead(in_dim=self.mlp_base.get_out_dim())
self.field_head_rgb = RGBFieldHead(in_dim=self.mlp_head.get_out_dim())
self.field_head_transient_uncertainty = UncertaintyFieldHead(in_dim=self.mlp_transient.get_out_dim())
self.field_head_transient_rgb = TransientRGBFieldHead(in_dim=self.mlp_transient.get_out_dim())
self.field_head_transient_density = TransientDensityFieldHead(in_dim=self.mlp_transient.get_out_dim())
[docs] def get_density(self, ray_samples: RaySamples) -> Tuple[Tensor, Tensor]:
"""Computes and returns the densities."""
encoded_xyz = self.position_encoding(ray_samples.frustums.get_positions())
encoded_xyz = self.position_encoding(ray_samples.frustums.get_positions())
base_mlp_out = self.mlp_base(encoded_xyz)
density = self.field_head_density(base_mlp_out)
return density, base_mlp_out
[docs] def get_outputs(
self, ray_samples: RaySamples, density_embedding: Optional[Float[Tensor, "*batch embedding_size"]] = None
) -> Dict[FieldHeadNames, Tensor]:
"""Returns the outputs of the NeRF-W field.
Args:
ray_samples: Ray samples.
density_embedding: Density embedding.
Returns:
Outputs of the NeRF-W field.
"""
outputs = {}
encoded_dir = self.direction_encoding(ray_samples.frustums.directions)
if ray_samples.camera_indices is None:
raise AttributeError("Camera indices are not provided.")
camera_indices = ray_samples.camera_indices.squeeze().to(ray_samples.frustums.origins.device)
mlp_in = [density_embedding, encoded_dir]
if self.embedding_appearance is not None:
embedded_appearance = self.embedding_appearance(camera_indices)
mlp_in.append(embedded_appearance)
mlp_head_out = self.mlp_head(torch.cat(mlp_in, dim=-1))
outputs[self.field_head_rgb.field_head_name] = self.field_head_rgb(mlp_head_out) # static rgb
embedded_transient = self.embedding_transient(camera_indices)
transient_mlp_in = torch.cat([density_embedding, embedded_transient], dim=-1) # type: ignore
transient_mlp_out = self.mlp_transient(transient_mlp_in)
outputs[self.field_head_transient_uncertainty.field_head_name] = self.field_head_transient_uncertainty(
transient_mlp_out
) # uncertainty
outputs[self.field_head_transient_rgb.field_head_name] = self.field_head_transient_rgb(
transient_mlp_out
) # transient rgb
outputs[self.field_head_transient_density.field_head_name] = self.field_head_transient_density(
transient_mlp_out
) # transient density
return outputs