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113 lines
4.8 KiB
Python
113 lines
4.8 KiB
Python
# Copyright 2022 The Plenoptix Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Classic NeRF field"""
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from typing import Dict, Optional, Tuple
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import torch
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from torch import nn
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from torchtyping import TensorType
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from nerfactory.cameras.rays import RaySamples
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from nerfactory.fields.base import Field
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from nerfactory.fields.modules.encoding import Encoding, Identity
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from nerfactory.fields.modules.field_heads import (
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DensityFieldHead,
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FieldHead,
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FieldHeadNames,
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RGBFieldHead,
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)
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from nerfactory.fields.modules.mlp import MLP
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from nerfactory.fields.modules.spatial_distortions import SpatialDistortion
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class NeRFField(Field):
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"""NeRF Field
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Args:
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position_encoding (Encoding, optional): Position encoder. Defaults to Identity(in_dim=3).
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direction_encoding (Encoding, optional): Direction encoder. Defaults to Identity(in_dim=3).
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base_mlp_num_layers (int, optional): Number of layers for base MLP. Defaults to 8.
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base_mlp_layer_width (int, optional): Width of base MLP layers. Defaults to 256.
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head_mlp_num_layers (int, optional): Number of layer for ourput head MLP. Defaults to 2.
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head_mlp_layer_width (int, optional): Width of output head MLP layers. Defaults to 128.
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skip_connections (Tuple, optional): Where to add skip connection in base MLP. Defaults to (4,).
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use_integrated_encoding (bool, optional): Used integrated samples as encoding input, Defaults to False.
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spatial_distortion (SpatialDistortion, optional): Spatial distortion. Defaults to None.
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"""
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def __init__(
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self,
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position_encoding: Encoding = Identity(in_dim=3),
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direction_encoding: Encoding = Identity(in_dim=3),
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base_mlp_num_layers: int = 8,
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base_mlp_layer_width: int = 256,
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head_mlp_num_layers: int = 2,
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head_mlp_layer_width: int = 128,
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skip_connections: Tuple[int] = (4,),
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field_heads: Tuple[FieldHead] = (RGBFieldHead(),),
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use_integrated_encoding: bool = False,
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spatial_distortion: Optional[SpatialDistortion] = None,
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) -> None:
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super().__init__()
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self.position_encoding = position_encoding
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self.direction_encoding = direction_encoding
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self.use_integrated_encoding = use_integrated_encoding
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self.spatial_distortion = spatial_distortion
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self.mlp_base = MLP(
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in_dim=self.position_encoding.get_out_dim(),
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num_layers=base_mlp_num_layers,
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layer_width=base_mlp_layer_width,
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skip_connections=skip_connections,
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)
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self.mlp_head = MLP(
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in_dim=self.mlp_base.get_out_dim() + self.direction_encoding.get_out_dim(),
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num_layers=head_mlp_num_layers,
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layer_width=head_mlp_layer_width,
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)
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self.field_output_density = DensityFieldHead(in_dim=self.mlp_base.get_out_dim())
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self.field_heads = nn.ModuleList(field_heads)
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for field_head in self.field_heads:
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field_head.set_in_dim(self.mlp_head.get_out_dim()) # type: ignore
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def get_density(self, ray_samples: RaySamples):
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if self.use_integrated_encoding:
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gaussian_samples = ray_samples.frustums.get_gaussian_blob()
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if self.spatial_distortion is not None:
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gaussian_samples = self.spatial_distortion(gaussian_samples)
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encoded_xyz = self.position_encoding(gaussian_samples.mean, covs=gaussian_samples.cov)
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else:
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positions = ray_samples.frustums.get_positions()
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if self.spatial_distortion is not None:
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positions = self.spatial_distortion(positions)
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encoded_xyz = self.position_encoding(positions)
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base_mlp_out = self.mlp_base(encoded_xyz)
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density = self.field_output_density(base_mlp_out)
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return density, base_mlp_out
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def get_outputs(
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self, ray_samples: RaySamples, density_embedding: Optional[TensorType] = None
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) -> Dict[FieldHeadNames, TensorType]:
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outputs = {}
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for field_head in self.field_heads:
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encoded_dir = self.direction_encoding(ray_samples.frustums.directions)
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mlp_out = self.mlp_head(torch.cat([encoded_dir, density_embedding], dim=-1)) # type: ignore
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outputs[field_head.field_head_name] = field_head(mlp_out)
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return outputs
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