Source code for nerfstudio.fields.semantic_nerf_field

# 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.
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Semantic NeRF field implementation.

from typing import Dict, Optional, Tuple

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, FieldHeadNames, RGBFieldHead, SemanticFieldHead
from nerfstudio.field_components.mlp import MLP
from nerfstudio.fields.base_field import Field

[docs]class SemanticNerfField(Field): """Semantic-NeRF field Args: num_semantic_classes: Number of distinct semantic classes. 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. """ def __init__( self, num_semantic_classes: 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, skip_connections: Tuple[int] = (4,), ) -> None: super().__init__() self.num_semantic_classes = num_semantic_classes self.position_encoding = position_encoding self.direction_encoding = direction_encoding 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_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.mlp_semantic = MLP( in_dim=self.mlp_head.get_out_dim(), layer_width=self.mlp_head.layer_width // 2, num_layers=1, activation=nn.ReLU(), 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_semantic = SemanticFieldHead( in_dim=self.mlp_semantic.get_out_dim(), num_classes=self.num_semantic_classes )
[docs] def get_density(self, ray_samples: RaySamples) -> Tuple[Tensor, Tensor]: 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[Tensor] = None ) -> Dict[FieldHeadNames, Tensor]: encoded_dir = self.direction_encoding(ray_samples.frustums.directions) mlp_out = self.mlp_head([encoded_dir, density_embedding], dim=-1)) # type: ignore outputs = {} # rgb outputs[self.field_head_rgb.field_head_name] = self.field_head_rgb(mlp_out) # semantic mlp_out_sem = self.mlp_semantic(mlp_out) outputs[self.field_head_semantic.field_head_name] = self.field_head_semantic(mlp_out_sem) return outputs