Reconstruction of a 3D image from layered 2D images

[Nihuepana]

I am currently looking at constructing a model of a 3D printed item based on a sequence of images of each layer of the print.

At the moment the sequence of images are transformed to black / white and run through a Moore Neighborhood algorithm and then a voxel representation of the printed item is generated, however I'd like to know what other approaches there would be. If anyone have any experience in this and could come up with some recommendations on what fields and algorithms I should look into I would be much obliged.

 

[pat_connell]

One approach you could consider is using a deep learning algorithm to generate a 3D model from a sequence of images. Deep learning algorithms such as convolutional neural networks (CNNs) have been used successfully for a variety of tasks, including image classification, object detection, semantic segmentation, and scene understanding. By training a CNN on a large dataset of images, the network can learn to identify patterns in the images that represent the 3D model and then use those patterns to generate a 3D representation. You could also use Generative Adversarial Networks (GANs), which are a type of generative model that can be trained to generate realistic 3D models from a given set of input images. Finally, you could also consider using traditional computer vision and image processing techniques such as edge detection, feature extraction, stereo vision, and 3D reconstruction to generate a 3D model from a sequence of images.