import torch
import torch.nn as nn
import torch.nn.functional as F
class SimpleNeRFMLP(nn.Module):
def __init__(self, input_dims=3, view_dir_dims=3, output_rgb_sigma_dims=4, hidden_dims=256, use_view_dirs=True):
super().__init__()
self.use_view_dirs = use_view_dirs
# Positional encoding is crucial for NeRF but omitted here for simplicity.
# Let's assume input_dims includes encoded position, and view_dir_dims includes encoded view direction.
# Initial layers for processing spatial location (x, y, z)
self.fc_layers_xyz = nn.Sequential(
nn.Linear(input_dims, hidden_dims), nn.ReLU(),
nn.Linear(hidden_dims, hidden_dims), nn.ReLU(),
nn.Linear(hidden_dims, hidden_dims), nn.ReLU(),
nn.Linear(hidden_dims, hidden_dims), nn.ReLU(),
)
# Layer to output sigma (volume density) and features for RGB prediction
self.sigma_and_feature_layer = nn.Sequential(
nn.Linear(hidden_dims, hidden_dims), nn.ReLU(), # Additional layer before sigma
nn.Linear(hidden_dims, 1 + hidden_dims) # Output: 1 for sigma, hidden_dims for RGB features
)
if self.use_view_dirs:
# Layers to process features and view direction for RGB
self.fc_layers_rgb = nn.Sequential(
nn.Linear(hidden_dims + view_dir_dims, hidden_dims // 2), nn.ReLU(),
nn.Linear(hidden_dims // 2, 3) # Output: 3 for RGB
)
else:
# Simpler RGB prediction if not using view directions (less realistic)
self.fc_layers_rgb_noview = nn.Sequential(
nn.Linear(hidden_dims, hidden_dims // 2), nn.ReLU(),
nn.Linear(hidden_dims // 2, 3) # Output: 3 for RGB
)
def forward(self, x_pos_encoded, view_dirs_encoded=None):
# x_pos_encoded: (batch_size, num_samples_along_ray, encoded_pos_dims)
# view_dirs_encoded: (batch_size, num_samples_along_ray, encoded_view_dir_dims) or (batch_size, 1, encoded_view_dir_dims)
xyz_features = self.fc_layers_xyz(x_pos_encoded)
sigma_and_features = self.sigma_and_feature_layer(xyz_features)
sigma = F.relu(sigma_and_features[..., 0:1]) # Volume density (should be non-negative)
rgb_features = sigma_and_features[..., 1:]
if self.use_view_dirs:
if view_dirs_encoded is None:
raise ValueError("view_dirs_encoded must be provided if use_view_dirs is True")
# Ensure view_dirs_encoded can be broadcast if it's per-ray rather than per-sample
if view_dirs_encoded.shape[1] == 1 and rgb_features.shape[1] > 1:
view_dirs_encoded = view_dirs_encoded.expand(-1, rgb_features.shape[1], -1)
combined_features = torch.cat([rgb_features, view_dirs_encoded], dim=-1)
raw_rgb = self.fc_layers_rgb(combined_features)
else:
raw_rgb = self.fc_layers_rgb_noview(rgb_features)
rgb = torch.sigmoid(raw_rgb) # RGB values usually in [0, 1]
return rgb, sigma
# --- Conceptual Volume Rendering (Highly Simplified) ---
# def volume_render_simplified(rgb_samples, sigma_samples, z_vals, white_bkgd=True):
# # rgb_samples: (batch_size, num_samples, 3)
# # sigma_samples: (batch_size, num_samples, 1)
# # z_vals: (batch_size, num_samples) - distances along ray
#
# deltas = z_vals[..., 1:] - z_vals[..., :-1] # Distance between adjacent samples
# # Assume last delta is large (goes to infinity)
# delta_inf = torch.full_like(deltas[..., :1], 1e10)
# deltas = torch.cat([deltas, delta_inf], dim=-1)
#
# # Alpha compositing: alpha = 1 - exp(-sigma * delta)
# alpha = 1. - torch.exp(-sigma_samples.squeeze(-1) * deltas) # (batch_size, num_samples)
#
# # Transmittance: T_i = product_{j=1 to i-1} (1 - alpha_j)
# # Or use cumprod: T_i = exp(-sum_{j=1 to i-1} sigma_j * delta_j)
# # For stability, use exclusive cumprod on (1-alpha)
# # weights_i = T_i * alpha_i
# transmittance = torch.cumprod(torch.cat([torch.ones_like(alpha[:, :1]), 1. - alpha + 1e-10], dim=-1), dim=-1)[:, :-1]
# weights = alpha * transmittance # (batch_size, num_samples)
#
# # Composite RGB values
# rgb_map = torch.sum(weights.unsqueeze(-1) * rgb_samples, dim=-2) # (batch_size, 3)
#
# if white_bkgd:
# acc_map = torch.sum(weights, dim=-1) # Accumulated opacity
# rgb_map = rgb_map + (1. - acc_map.unsqueeze(-1)) # Add white background
#
# return rgb_map
# Example Usage (Conceptual - requires actual positional encoding, ray sampling etc.)
# BATCH_SIZE = 4
# NUM_SAMPLES_PER_RAY = 64
# POS_ENC_DIMS = 63 # Example: (3 original dims * 2 * 10 L) + 3 original
# VIEW_ENC_DIMS = 27 # Example: (3 original dims * 2 * 4 L) + 3 original
# mlp = SimpleNeRFMLP(input_dims=POS_ENC_DIMS, view_dir_dims=VIEW_ENC_DIMS)
# dummy_pos_encoded = torch.randn(BATCH_SIZE, NUM_SAMPLES_PER_RAY, POS_ENC_DIMS)
# dummy_view_encoded = torch.randn(BATCH_SIZE, NUM_SAMPLES_PER_RAY, VIEW_ENC_DIMS) # Or (BATCH_SIZE, 1, VIEW_ENC_DIMS)
# rgb_out, sigma_out = mlp(dummy_pos_encoded, dummy_view_encoded)
# print("RGB output shape:", rgb_out.shape) # (BATCH_SIZE, NUM_SAMPLES_PER_RAY, 3)
# print("Sigma output shape:", sigma_out.shape) # (BATCH_SIZE, NUM_SAMPLES_PER_RAY, 1)
# dummy_z_vals = torch.linspace(0, 1, NUM_SAMPLES_PER_RAY).unsqueeze(0).expand(BATCH_SIZE, -1)
# final_pixel_color = volume_render_simplified(rgb_out, sigma_out, dummy_z_vals)
# print("Final pixel color shape:", final_pixel_color.shape) # (BATCH_SIZE, 3)