3D imaging data has been in the limelight for the past couple of years in the fields of Medical Imaging, Robotics, Autonomous Vehicles and computer vision. Artificial Intelligence, Machine Learning and Deep Learning helps us in the development of models that are used for Recognition, Classification and Translation of Data. While these techniques have been in the technical industry for a decade, the complexities of working on 3D imaging data have been a major problem when it comes to processing 3D data and executing run-time applications that uses data formats such as 3D Point Clouds or Meshes. A major aspect of 3D image data is the depth map where the same data is represented in a 2-Dimensional format known as a depth map. This 2D representation of the 3D point cloud has the same data and is converted into a lossless format, that is no data or state of the defined object is lost. In this paper we propose a novel method of converting 3D Point Clouds and Meshes into 2D Depth Images also called as Depth Map. This research focuses on a mathematical model that helps in this conversion. The mathematical model that we developed converts the file in the least time possible. Thus, this research aims at solving the problem of converting 3D image data into 2D depth maps with no information loss, converting at high speed and less time complexities.

With the development of sensor technology for 3D imaging, the applications that involve use of these images have become a center of attraction for many developers. Current 2D image-based applications are being updated 3D imaging technology. With LiDAR’s and Dot Projectors paving their ways in smart phones, laptops and IoT devices, 3D imaging technology has become cheap and easily accessible. The importance of 3D imaging data is seen in medical applications, autonomous cars and robotics where precision is more important than efficiencies of the system. The precisions are obtained when a good amount of data is present of the surrounding and environment that the particular application is working with. The drawback of using 3D imaging data is that the current sensors collect numerous data which is difficult to process and consumes a lot of memory space. Processors which have the ability to process such data at high speeds are expensive and also have compatibility problems. Point Clouds and Meshes are the two widely used formats for applications based on 3D data. In this paper, we propose efficient methods that help us in reducing the noise, removal of unnecessary data and lossless compression of 3D image files that helps in efficient storage of files in the databases. The methods proposed in this paper also help in understanding the preprocessing steps for any recognition or classification applications that use Point Clouds or Meshes.