Deep Patch Visual Odometry

08/08/2022

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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.

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Deep Patch Visual Odometry

Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction

DSOL: A Fast Direct Sparse Odometry Scheme

Mono-hydra: Real-time 3D scene graph construction from monocular camera input with IMU

Reducing Drift in Visual Odometry by Inferring Sun Direction Using a Bayesian Convolutional Neural Network

LoRCoN-LO: Long-term Recurrent Convolutional Network-based LiDAR Odometry

Simultaneously Learning Corrections and Error Models for Geometry-based Visual Odometry Methods

OCC-VO: Dense Mapping via 3D Occupancy-Based Visual Odometry for Autonomous Driving

Deep Patch Visual Odometry

Related Research

Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction

DSOL: A Fast Direct Sparse Odometry Scheme

Mono-hydra: Real-time 3D scene graph construction from monocular camera input with IMU

Reducing Drift in Visual Odometry by Inferring Sun Direction Using a Bayesian Convolutional Neural Network

LoRCoN-LO: Long-term Recurrent Convolutional Network-based LiDAR Odometry

Simultaneously Learning Corrections and Error Models for Geometry-based Visual Odometry Methods

OCC-VO: Dense Mapping via 3D Occupancy-Based Visual Odometry for Autonomous Driving