# 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,
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"""Classic NeRF field"""
from typing import Dict, Optional, Tuple, Type
import torch
from torch import Tensor, nn
from nerfstudio.cameras.rays import RaySamples
from nerfstudio.field_components.encodings import Encoding, Identity
from nerfstudio.field_components.field_heads import DensityFieldHead, FieldHead, FieldHeadNames, RGBFieldHead
from nerfstudio.field_components.mlp import MLP
from nerfstudio.field_components.spatial_distortions import SpatialDistortion
from nerfstudio.fields.base_field import Field
[docs]class NeRFField(Field):
"""NeRF Field
Args:
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.
skip_connections: Where to add skip connection in base MLP.
use_integrated_encoding: Used integrated samples as encoding input.
spatial_distortion: Spatial distortion.
"""
def __init__(
self,
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,
skip_connections: Tuple[int] = (4,),
field_heads: Optional[Tuple[Type[FieldHead]]] = (RGBFieldHead,),
use_integrated_encoding: bool = False,
spatial_distortion: Optional[SpatialDistortion] = None,
) -> None:
super().__init__()
self.position_encoding = position_encoding
self.direction_encoding = direction_encoding
self.use_integrated_encoding = use_integrated_encoding
self.spatial_distortion = spatial_distortion
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.field_output_density = DensityFieldHead(in_dim=self.mlp_base.get_out_dim())
if field_heads:
self.mlp_head = MLP(
in_dim=self.mlp_base.get_out_dim() + self.direction_encoding.get_out_dim(),
num_layers=head_mlp_num_layers,
layer_width=head_mlp_layer_width,
out_activation=nn.ReLU(),
)
self.field_heads = nn.ModuleList([field_head() for field_head in field_heads] if field_heads else []) # type: ignore
for field_head in self.field_heads:
field_head.set_in_dim(self.mlp_head.get_out_dim()) # type: ignore
[docs] def get_density(self, ray_samples: RaySamples) -> Tuple[Tensor, Tensor]:
if self.use_integrated_encoding:
gaussian_samples = ray_samples.frustums.get_gaussian_blob()
if self.spatial_distortion is not None:
gaussian_samples = self.spatial_distortion(gaussian_samples)
encoded_xyz = self.position_encoding(gaussian_samples.mean, covs=gaussian_samples.cov)
else:
positions = ray_samples.frustums.get_positions()
if self.spatial_distortion is not None:
positions = self.spatial_distortion(positions)
encoded_xyz = self.position_encoding(positions)
base_mlp_out = self.mlp_base(encoded_xyz)
density = self.field_output_density(base_mlp_out)
return density, base_mlp_out
[docs] def get_outputs(
self, ray_samples: RaySamples, density_embedding: Optional[Tensor] = None
) -> Dict[FieldHeadNames, Tensor]:
outputs = {}
for field_head in self.field_heads:
encoded_dir = self.direction_encoding(ray_samples.frustums.directions)
mlp_out = self.mlp_head(torch.cat([encoded_dir, density_embedding], dim=-1)) # type: ignore
outputs[field_head.field_head_name] = field_head(mlp_out)
return outputs