Abstract

The past decade has seen a revolution in the ability of machines to extract high-level, semantic meaning from raw sensory data. This revolution has primarily been brought about by deep learning. In combination with exponential growth in the availability of compute and the rapidly decreasing cost of sensing, this has been an explosive change to the field of digital mapping. The 3D Vision team of Apple Maps capitalizes on these trends to push the boundaries of mapping. By using state-of-the-art technologies from machine learning and computer vision, we can redefine the modern digital map and how it is built. In the talk, we will walk you through our end-to-end philosophy for large-scale mapping in the age of deep learning. We take a look under the hood with examples and visualizations to explain how our approach has led to many breakthrough products like Look Around, Visual Localization for Geo-Referenced AR, and the new 3D Apple Maps.

Martin Byröd

Engineering Manager @ Apple

Martin Byröd is head of R&D for the 3D Vision team at Apple Maps, where he leads research and development for processing large-scale 3D sensor data (imagery and lidar) for mapping and beyond. Martin obtained his PhD in Mathematics from Lund University in June 2010 in the areas of Applied Mathematics and Computer Vision. After graduation, he joined the Swedish startup C3 Technologies, where he worked on large-scale image-based 3D reconstruction. The past ten years have been spent at Apple, where he has led core R&D for products such as Flyover 3D, Look Around, and Visual Localization.