Machine Learning Current and Future in 3D Visualisation
Machine learning model management and techniques are being extensively used in 3D visualisation, but this research field is naive. Some of the recent use of 3D Machine learning visualisations in different fields are discussed below.
Protein Structure Prediction
Google DeepMind Program initiated the AlphaFold project for a critical assessment of structure prediction of Proteins. Protein-structure prediction results show that using ML and deep learning algorithms the protein structures are accurately predicted in 3D visualisation. This will aid many areas of life sciences and medicines field in diseases, cell representation, and transformation
Visualization of Geographical Areas
Machine learning algorithms are fast on 3D data visualisation and analysis, especially for geographical areas. Now powerful ML algorithms are used with 3D to visualise aerial and satellite images. Radars and lasers are used to create 3D visual representations in geographical areas. Now the planning on the big stage has been changed. This change includes better resource allocation of health, education, and others can be easily possible by analyzing the geography.
3D Designing and Modelling in the Construction Industry
ML algorithms in 3D visualisation are changing the construction and civil design industry very fast. Now powerful AutoCAD tools having machine learning algorithms integrated and 3D visualization features help in designing different models for the construction industry and visualizing them on 3Ds. The virtual models on geographic locations using 3D imaging look real and are accurate.
3D Visualisation of Medical Imaging
Machine learning models are aiding in the visualisation of 3D images of Computed Tomography, X-rays, micro and macro computed tomography, (MRI) Magnetic Resonance Imaging and others. Higher imaging resolutions of 3D with the power of ML analytics allows doctors to know about the details of organs without surgery. Many tests in the future will become obsolete and images can detect the stages of diseases of any deficiency patients face.
Urban Planning and Designing
The city and regional planning fields are changing so fast that automatic transformation and detection, 3D modelling, images extraction with features and visualisation of buildings are carried out on laptops by using ML algorithms and 3D visualisation. Urban and rural planning, designing, change detection, geographical visualization, mapping, information update, monitoring, house valuation and navigation can easily be done using 3D visualisation with ML support.
Fluorescently Labelled Cells
The ML with 3D visualisation has changed the whole dimensions of molecule-scale processes due to better visualisation and predictions than a human being. The ML models and 3D visualisation in this field is new and will take time to improve efficiency in cell scale visualisation and molecular scaling.
3D Athlete Pose Tracking
Amazon SageMaker estimates 3D posture used for 3D Athlete Tracking in preparation of different games. 3DAT is a machine learning (ML) in support of 3D visualisation to produce real-time images and videos for athletes to show where they are lacking in movements.
3D Bioprinting is a method of designing biomedical equipment using cells using 3D printing-like technology to closely resemble real-tissue features. In 3D printing and Bioprinting, ML and 3D visualisation is helping in medical process optimization, accuracy analysis, fault identification and prediction of material property prediction.
Texture classification involves learning texture and patterns from user-defined markers to categorize each pixel based on resemblance to learning accurate patterns in an image. The colour auto-classification uses machine learning with 3D visualisation to automatically separate different colour pictures into labels.
Image Segmentation of Mitochondria Blobs
ML in aid with 3D visualisation is helping to automate the extraction of mitochondria from the FIB-SEM stack that cannot be possible easily. Few slices of the image were used in Machine learning training which is segmented using the segment editor, software and the rest of the segmentation is done automatically saving a lot of time and resources.
3D Visualisation of X-ray Data
Machine learning algorithms working with 3D images have a powerful ability of 3D visualization was evaluated using gold particles and it is proved that the computational approach is a thousand times quicker and accurate as compared to other techniques. This testing also shows that ML algorithms can reconstruct missing information or images which cannot be detected by the sensor or detector.
3D Visualisation of Advanced Photon
A group of scientists at Argonne National Laboratory created a novel approach for translating X-ray data into 3D visualisation pictures using ML and 3D. The creation of 3D images and visualisation is a hundred times faster and reliable than the current approaches available.