By which we could study images (CNN) and point cloud (DGCNN) datas in the same time in Deep learning?

One way to study images and point cloud data simultaneously in deep learning is to use a multi-modal architecture that can handle both types of data. One approach is to incorporate a Convolutional Neural Network (CNN) for image analysis and a Dynamic Graph Convolutional Neural Network (DGCNN) for point cloud analysis within the same model. This can be achieved by designing a hybrid architecture that combines the strengths of both CNNs and DGCNNs, allowing for joint processing of image and point cloud data. By leveraging the hierarchical and spatial properties of CNNs for images and the graph-based structure of DGCNNs for point clouds, this combined model can effectively learn and extract features from both types of data, enabling comprehensive analysis and understanding in deep learning tasks.

Muhammad Azam

To study both images and point cloud data simultaneously in deep learning, you can use a combination of Convolutional Neural Networks (CNNs) and Graph Convolutional Neural Networks (GCNNs). Here's a general approach:

Input Representation:For images, you can use a CNN to process the image pixels and extract features hierarchically through convolutional and pooling layers. For point cloud data, you can represent it as a graph structure, where each point becomes a node in the graph, and edges represent the relationships between points. Each node in the graph can have attributes like coordinates, color, or other features.

CNN for Image Processing:Pass the image data through the CNN layers to extract meaningful features from the images. The CNN layers can consist of convolutional, pooling, and fully connected layers to capture spatial hierarchies and learn relevant image representations.

GCNN for Point Cloud Processing:Use a Graph Convolutional Neural Network (GCNN), such as DGCNN (Dynamic Graph CNN), to process the point cloud data. The GCNN layers can operate directly on the graph structure, taking into account the relationships between points and their features. GCNN layers typically include graph convolutional layers, graph pooling layers, and fully connected layers designed to handle graph data.

Fusion and Integration:After processing the image and point cloud data separately, you can integrate the extracted features. This can be done by concatenating or combining the learned features from both the CNN and GCNN into a single representation. You can then feed this joint representation into additional fully connected layers for classification, regression, or any other downstream tasks.

By combining CNNs for image processing and GCNNs for point cloud processing, you can leverage the strengths of both models to study and analyze images and point cloud data simultaneously in deep learning.

What is your perception on using visual analysis or quantitative approaches to evaluate data from single-case research designs (n of 1 trials)?

What is the state of the drug inside solid lipid nanoparticles (SLNs)?

How can I make stacked bar graph with scatter plot?

What are the application of Fixed points thoery in Chemistry.?

What could be reason for getting such type of graph for complex impedance in case of polymer blend films?

Can I use a graph from a another source in a thesis?

I have need of XRD graph analysis software for phase identification. Can you please provide links for software tools which can be downloaded?

Could you please suggest to me research in which a relation between the shear wave velocity and the degree of saturation for stone was discussed?

Is it possible, for a limited dateset of images, to use Convolution filters + Random Forest instead of UNet?

Algorithms to filter corrective max and min from a set of data?