Practical 2D to 3D Image Conversion

Adam Rowell


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Date: May 13, 2020


We will discuss techniques for converting 2D images to 3D meshes on mobile devices.  This includes methods to efficiently compute both dense and sparse depth maps, converting depth maps into 3D meshes, mesh inpainting, and post-processing.  We focus on different CNN designs to solve each step in the processing pipeline and examine common failure modes.  Finally, we will look at practical deployment of image processing algorithms and CNNs on mobile apps, and how Lucid uses cloud processing to balance processing power with latency.

Further Information:

Adam is the co-founder and CTO of Lucid. His breakthrough research in computer vision and signal processing as a PhD at Stanford powers the technology behind Lucid’s 3D Fusion Technology, world’s first AI-based 3D and depth capture technology mimicking human vision in dual/multi camera devices which is currently deployed in their own product, the LucidCam, in most of mobile phones, robots, and aiming to be in autonomous cars. He worked for many years in the industry for Exponent as a consultant focused on machine learning and computer vision development, from consumer to business to military applications, coding and leading engineering teams to build the most advanced GPU/NPU based systems in the industry. Afterwards, he joined Maxim Integrated in their Advanced Analytics team, optimizing the organization of the 10,000 employee public company from the ground up. Adam defines the technology direction and leads Lucid’s engineering team

Created: Saturday, May 23rd, 2020