Training PackNet-SFM with RGB + PointCloud Data

Training PackNet-SFM with RGB + PointCloud Data

Fusing time synchronized RGB camera images with point cloud data is a powerful approach for training a PackNet-SFM (Structure from Motion) model in PyTorch. This technique combines the benefits of both RGB images and point cloud data to improve the accuracy and robustness of the SFM model. In this article, we will discuss the various stages of fusing time synchronized RGB camera images with point cloud data and provide example code in numpy, pCL, and PyTorch.

import torch
import torch.nn as nn
import numpy as np
from import DataLoader

# Define the PackNet-SFM model
class PackNetSFM(nn.Module):
    def __init__(self):
        super(PackNetSFM, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.fc1 = nn.Linear(128 * 16 * 16, 1024)
        self.fc2 = nn.Linear(1024, 3)

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = x.view(x.size(0), -1)
        x = self.fc1(x)
        x = self.fc2(x)
        return x

# Load ground truth point cloud data
ground_truth = np.load('ground_truth.npy')

# Define the loss function and optimizer
model = PackNetSFM()
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# Define the dataset and dataloader
class PointCloudDataset(
    def __init__(self, data): = data

    def __getitem__(self, index):

    def __len__(self):
        return len(

dataset = PointCloudDataset(ground_truth)
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)

# Train the model
for epoch in range(100):
    for i, data in enumerate(dataloader):
        inputs, labels = data
        outputs = model(inputs)
        loss = criterion(outputs, labels)
    print('Epoch [%d/100], Loss: %.4f' % (epoch+1, loss.item()))

# Save the trained model, 'packnet_sfm.pth')

In this example, we first define the PackNet-SFM model using a combination of convolutional and fully connected layers. We then load the ground truth point cloud data and define the loss function and optimizer. Next, we create a dataset and dataloader using the ground truth point cloud data and use these to train the model for 100 epochs.

Finally, we save the trained model to a file for future use. Note that in order to use the trained model, one needs to first construct the PackNetSFM object and load the weights from the saved file.


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