Estimating the Relative Changes in Motion of 3D Objects

CNN for estimating the relative changes in motion between two consecutive images.of 3D objects

This MATLAB code demonstrates the construction and training of a simple Convolutional Neural Network (CNN) for estimating the relative changes in motion between two consecutive images of 3D objects. Here is a summary of the code:

1. Load the dataset and ground truth data, where 'input_images' is a 4D array containing image pairs and 'ground_truth' is a 2D array representing the 6x1 array of translations and rotations.
2. Split the data into training, validation, and testing sets based on a ratio (70% training, 20% validation, 10% testing).
3. Create the CNN architecture with the following layers:
4. - Input layer with two images (size of input_images, 1, size of input_images, 2, size of input_images, 3, 2)
5. - Convolutional layers with 16 and 32 filters of size 3x3, followed by batch normalization and ReLU activation.
6. - Max pooling layers for downsampling.
7. - Fully connected layers with 128 and 6 neurons (output layer for translations and rotations) and a regression layer for continuous output.
8. Set the training options using the Adam optimizer, specifying the number of epochs, mini-batch size, and validation data for monitoring progress.
9. Train the CNN using the training data and labels, specified layers, and training options.
10. Test the CNN on the testing dataset and obtain predicted labels.
11. Calculate the Mean Squared Error (MSE) between predicted and ground truth labels to evaluate the model's performance.

• Note: The code provided is a basic template and may need to be adjusted based on the specifics of the dataset, desired CNN architecture, and hyperparameters for optimal performance. Additionally, the dataset 'input_images' and 'ground_truth' need to be replaced with actual data before running the code.
• Please note that this is a simple example of a CNN architecture, and you may need to adjust the number of layers, filter sizes, and other hyperparameters based on the complexity of your task and the characteristics of your dataset. The actual design of the CNN may require experimentation and fine-tuning to achieve the best results for your specific application. Additionally, you can consider using more advanced CNN architectures such as ResNet or VGG for better performance if needed.