Deep Patch Visual Odometry

Zachary Teed, Lahav Lipson, Jia Deng

Keywords: Visual Odometry, SLAM, Simultaneous Localization and Mapping, 3D, Structure from Motion

TL;DR: We propose a new deep learning system for monocular Visual Odometry.

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.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics

Submission Guidelines: Yes

Please Choose The Closest Area That Your Submission Falls Into: Applications (eg, speech processing, computer vision, NLP)

Community Implementations: [ 3 code implementations](https://www.catalyzex.com/paper/deep-patch-visual-odometry/code)