【三维重建】【深度学习】NeuS代码Pytorch实现--训练阶段代码解析(上)

**三维重建与深度学习**

在计算机视觉领域，三维重建是指从二维图像中恢复出真实世界中的三维场景。深度学习是一种基于人工神经网络的机器学习方法，可以用于解决多个问题，包括图像分类、目标检测等。在本文中，我们将讨论如何使用PyTorch实现NeuS（Neural Surface Reconstruction）代码，特别是训练阶段。

**NeuS**

NeuS是一种基于深度学习的三维重建方法，它可以从单张二维图像中恢复出真实世界中的三维场景。NeuS使用一个神经网络来预测每个像素点的3D坐标。

**PyTorch实现**

在本文中，我们将使用PyTorch实现NeuS代码。在训练阶段，我们需要准备数据集、定义模型结构、设置训练参数等。

### 准备数据集首先，我们需要准备一个包含多张图像的数据集，每张图像对应一个真实世界中的场景。我们可以使用一些现有的数据集，如KITTI或Stanford3D Scanning Dataset。

import osfrom torch.utils.data import Dataset, DataLoaderclass MyDataset(Dataset):
 def __init__(self, data_dir, transform=None):
 self.data_dir = data_dir self.transform = transform self.images = []
 self.depths = []

 for file in os.listdir(data_dir):
 if file.endswith(".jpg"):
 self.images.append(os.path.join(data_dir, file))
 self.depths.append(os.path.join(data_dir, file.replace(".jpg", ".png")))

 def __len__(self):
 return len(self.images)

 def __getitem__(self, idx):
 image = Image.open(self.images[idx])
 depth = Image.open(self.depths[idx])

 if self.transform:
 image = self.transform(image)
 depth = self.transform(depth)

 return image, depth

import torch.nn as nnclass NeuS(nn.Module):
 def __init__(self):
 super(NeuS, self).__init__()
 self.conv1 = nn.Conv2d(3,64, kernel_size=7)
 self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2)
 self.conv2 = nn.Conv2d(64,128, kernel_size=5)
 self.pool2 = nn.MaxPool2d(kernel_size=3, stride=2)
 self.fc1 = nn.Linear(128 *7 *7,512)
 self.fc2 = nn.Linear(512,3)

 def forward(self, x):
 out = self.conv1(x)
 out = torch.relu(out)
 out = self.pool1(out)

 out = self.conv2(out)
 out = torch.relu(out)
 out = self.pool2(out)

 out = out.view(-1,128 *7 *7)
 out = torch.relu(self.fc1(out))
 out = self.fc2(out)
 return out

import torch.optim as optim# Define the device (GPU or CPU)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# Initialize the model, loss function and optimizermodel = NeuS()
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Set the batch size and number of epochsbatch_size =32num_epochs =10# Create a data loader for the training settrain_loader = DataLoader(MyDataset(data_dir="path/to/train/directory"), batch_size=batch_size, shuffle=True)

for epoch in range(num_epochs):
 for i, (images, depths) in enumerate(train_loader):
 # Move the data to the device (GPU or CPU)
 images = images.to(device)
 depths = depths.to(device)

 # Zero the gradients optimizer.zero_grad()

 # Forward pass outputs = model(images)
 loss = criterion(outputs, depths)

 # Backward pass loss.backward()

 # Update the model parameters optimizer.step()

 # Print the loss at each iteration print(f"Epoch {epoch+1}, Iteration {i+1}, Loss: {loss.item():.4f}")