Deep Patch Visual Odometry

Zachary Teed, Lahav Lipson, Jia Deng

Abstract: We propose Deep Patch Visual Odometry (DPVO), a new deep learning system for monocular Visual Odometry (VO). DPVO is accurate and robust while running at 2x-5x real-time speeds on a single RTX-3090 GPU using only 4GB of memory. We perform evaluation on standard benchmarks and outperform all prior work (classical or learned) in both accuracy and speed. Code is available at https://github.com/princeton-vl/DPVO.