360DVO Dataset

This is the official dataset from the paper 360DVO: Deep Visual Odometry for Monocular 360-Degree Camera, which is published on IEEE Robotics and Automation Letters (RA-L) 2026.

Project Page | Paper | Code

360DVO is a deep learning-based omnidirectional visual odometry (OVO) framework. It introduces a distortion-aware spherical feature extractor (DAS-Feat) and an omnidirectional differentiable bundle adjustment (ODBA) module. This repository provides the real-world OVO benchmark introduced in the study to facilitate evaluation in realistic settings.

• Updates

• Jan. 9th, 2026: we update the names of each sequence for better understanding.
• Jan. 6th, 2026: we add project page and arxiv paper link.
• Jan. 4th, 2026: we release the 360DVO dataset with ground truth trajectories.