3D Point Cloud Semantic Segmentation Using Deep Learning Techniques

Point cloud learning has recently attracted attention due to the development of Augmented Reality / Virtual Reality and its wide applications in areas of computer vision, autonomous driving, and robotics. Deep Learning has been successfully used to solve 2D vision problems, however, the use of deep learning techniques on point clouds is still in its infancy because of the unique challenges faced for its processing. Earlier approaches in Deep Learning overcome this challenge by pre-processing the point cloud into a structured grid format at the cost of increased computational cost or loss of depth information. 3D point cloud segmentation is the process of classifying point clouds into different homogeneous regions such that the points in the same isolated and meaningful region have similar properties. 3D segmentation is a challenging task because of high redundancy, uneven sampling density, and lack of explicit structure of point cloud data. <a href="https://medium.com/analytics-vidhya/3d-point-cloud-semantic-segmentation-using-deep-learning-techniques-6c4504a97ce6">Click Here</a>