How to train neural networks on LiDAR point cloud data

Mohammad Musa

(Deepen AI)


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Date: October 10, 2018

Description:

Accurate LiDAR classification and segmentation is required for developing critical ADAS & Autonomous Vehicles components. Mainly, its required for high definition mapping and developing perception and path/motion planning algorithms. This talk will cover best practices for how to accurately annotate and benchmark your AV/ADAS models against LiDAR point cloud ground truth training data.

Further Information:

Mohammad Musa started Deepen AI in January 2017 focusing on AI tools and infrastructure for the Autonomous Development industry. Mohammad used to lead product efforts for Google wide Initiatives to enable teams to build excellent products. He worked specifically on infrastructure products for tracking user centered metrics, bug management and user feedback loops. Prior to that, he was the head of Launch & Readiness at Google Apps for Work where he lead a cross functional team managing product launches, product roadmap, trusted tester and launch communications. Before Google, Mohammad worked in software engineering and technical sales positions in the video games and semiconductor industries in multiple startups.




Created: Wednesday, October 10th, 2018