3D Point Cloud analysis using Deep Learning
Processing 3D images has many use cases. For example, to improve autonomous car driving, to enable digital conversions of old factory buildings, to enable augmented reality solutions for medical surgeries, etc. Also 3D images help in 3D modeling and safety evaluation of products.
3D image processing brings enormous benefits but also amplifies computing cost. The size of the point cloud, the number of points, sparse and irregular point cloud, and the adverse impact of the light reflections, (partial) occlusions, etc., make it difficult for engineers to process point clouds.
Moving from using hand crafted features to using deep learning techniques to semantically segment the images, to classify objects, to detect objects, to detect actions in 3D videos, etc., we have come a long way in 3D image processing.
3D Point Cloud image processing is increasingly used to solve Industry 4.0 use cases to help architects, builders and product managers. I will share some of the innovations that are helping the progress of 3D point cloud processing. I will share the practical implementation issues we faced while developing deep learning models to make sense of 3D Point Clouds.
Attendees: Beginners and Intermediate skilled in Image Processing and 3D Point Clouds
Profile of the speaker:
SK Reddy is the Chief Product Officer AI in Hexagon (www.hexagon.com). He is an AI and ML expert and a successful twice startup entrepreneur. He is an AI startup advisor too. Also he is a frequent speaker in conferences and is an AI blogger.
YouTube Channel: https://www.youtube.com/user/skreddy99/videos
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