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3D machine learning: one of the most researched topics that have gained tremendous attention in recent years.

An amalgamation of machine learning, computer vision, and computer graphics is what completes the 3D machine learning model.

The term “3D machine learning” gained its popularity due to the recent ongoing research in areas like self-driving vehicles, augmented reality, virtual reality, and autonomous robots. The main objective of 3D machine learning is not just to analyze the individual models but to analyze the entire collections of these models using maps or correspondences, and computing alignments, etc.

For better understanding, we will look at some of the 3D models.

3D Object Detection:

This is in contribution to the PASCAL3D+ (2014) dataset which is a challenging dataset meant for 3D object detection and pose estimation.

These methods have gained popularity since they can easily manage uncertainties even in 2D images to provide a better description of the objects as compared to 2D detectors.

The PASCAL3D+ images provide better variability as compared to the already existing 3D datasets.

3D Object Detection

12 categories in total, and on average, provides 3K+ objects per category for the 3D object detection and pose estimation.

A Large number of datasets scanned:

A dataset of over ten thousand 3D scans of real objects was created. Around 70 operators equipped with consumer-grade mobile 3D scanning setups were asked to scan objects of their choice in their environments without any kind of supervision from the computer vision professionals. As a result, large and diverse objects were collected – toys, grand piano, shoes, mugs, vases, and construction vehicles, etc. This task was carried out with the help of the attorney to ensure the data acquisition do not undergo any type of privacy constraints. This dataset along with the reconstructed models and RGB-D scans are open for public usage with proper attribution.

A Large number of datasets scanned

A total of 10k scans in RGBD were made along with the reconstructed 3D models in .PLY format.

If you dig deeper, you will find other multiple information on 3D machine learning models, e.g. ABC: A Big CAD Model Dataset for Geometric Deep Learning, ShapeNet, and Semantic3D, etc.

Well, such model creation won’t be possible if it was not for the support of Python libraries. Let us further talk about the top Python libraries used for 3D machine learning.

1. Panda3D

An open-source library specifically used for 3D games, simulations, and visualizations. Written in C++ along with Python bindings, this library is accountable for gloss mapping, normal mapping, cartoon shading and inking, and HDR, etc.

Best features:

  • A cross-platform engine that eases deployment on most of the supported platforms.
  • It also combines command-line tools that help in optimizing and processing source assets.
  • In combination with C++ and Python, you get a faster rate of development without needing to compromising on the performance.

Stars on GitHub –2.3k

2. PyTorch3D:

Another open-source library used for 3D deep learning. It is highly optimized and highly modular and is specially designed to make 3D deep learning much easier using the PyTorch library. This library is used by Facebook AI research to boost research projects like Mesh R-CNN.

Best features:

  • Provides data structure to help store and manipulate triangle meshes.
  • PyTorch3D offers a differentiable mesh renderer.
  • Supported by a whole bunch of cloud platforms that provide easy scaling and frictionless development.

Stars on GitHub – 3.4k

3. Open3D:

An open-source library ideally used for 3D data processing which also supports the rapid development of software that deals with 3D data. The front-end of Open3D is exposed to a set of selected data structures and algorithms in both the languages – C++ and Python. However, the back-end is highly optimized and the set-up is used for parallelization.

Best features:

  • 3D visualization
  • 3D data processing algorithms
  • 3D data structures
  • Physically-based rendering (PBR)
  • Scene reconstruction

Stars on GitHub – 3.1k

4. Mayavi:

A cross-platform tool used for 3D scientific data visualization. It is open-source and is completely written in Python language. Mayavi provides a clean scripting interface to have an easy and interactive visualization of 3D data. Besides a clean scripting interface, this library also provides an object-oriented programming interface, one-liners, and many more other features.

Best features:

  • Offers easy extendibility through modules, custom sources, and data filters.
  • Helps in reading multiple file-formats including PLOT3D and VTK (legacy and XML).
  • Saves rendered visualizations in multiple image formats.

Stars on GitHub –747

5. pi3d:

It is a Python module that aims at simplifying writing 3D in Python language while also allowing them access to raspberry Pi GPU. The pi3d easily enables both 2D and 3D rendering while providing access to host multiple commands to load animated models and textured models, and to create shaders and fractal landscapes, etc.

Best features:

  • Apart from Raspberry Pi GPU, pi3d also runs well on Linux using X server or Windows using Pygame and on Android using python for android.
  • pi3d offers compatibility features in both Python 2 and Python 3.

Stars on GitHub – 257

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