Extending Deep Event Visual Odometry with Sparse Point-Cloud Export
Event cameras are well suited for visual odometry under high-speed motion and challenging lighting conditions due to their low latency, high temporal resolution, and high dynamic range. Deep Event Visual Odometry (DEVO) demonstrated that monocular event-only odometry can achieve strong performance by combining sparse patch tracking, learned patch selection, recurrent correspondence refinement, and differentiable bundle adjustment. In this project, we extend DEVO with a sparse point-cloud export pipeline. Rather than modifying the core odometry formulation, our approach exposes the internal 3D structure already estimated by DEVO and converts it into an explicit point-cloud representation for visualization and further processing. In addition, we implement a practical workflow for data export, format conversion, and point-cloud cleanup. The resulting system preserves the original visual odometry pipeline while enabling sparse geometric scene output. Experiments on the BOARD SLOW sequence show that the exported sparse cloud is locally consistent with EMVS reconstructions, achieving high precision at a 5 cm threshold, while also highlighting the expected limitations in density, completeness, and sensitivity to accumulated odometry noise.
Article intelligence
Key points
- Event cameras excel in high-speed and low-light conditions for odometry.
- DEVO achieves strong monocular event odometry via sparse tracking and bundle adjustment.
- This work adds a point-cloud export pipeline without altering core odometry.
- Exported cloud shows local consistency but limited density and completeness.
Why it matters
This matters because event cameras excel in high-speed and low-light conditions for odometry.
Technical impact
May affect agent architecture, tool calling, workflow automation, and product integration.
[2605.22890] Extending Deep Event Visual Odometry with Sparse Point-Cloud Export
[Submitted on 21 May 2026]
Title:Extending Deep Event Visual Odometry with Sparse Point-Cloud Export
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Abstract:Event cameras are well suited for visual odometry under high-speed motion and challenging lighting conditions due to their low latency, high temporal resolution, and high dynamic range. Deep Event Visual Odometry (DEVO) demonstrated that monocular event-only odometry can achieve strong performance by combining sparse patch tracking, learned patch selection, recurrent correspondence refinement, and differentiable bundle adjustment. In this project, we extend DEVO with a sparse point-cloud export pipeline. Rather than modifying the core odometry formulation, our approach exposes the internal 3D structure already estimated by DEVO and converts it into an explicit point-cloud representation for visualization and further processing. In addition, we implement a practical workflow for data export, format conversion, and point-cloud cleanup. The resulting system preserves the original visual odometry pipeline while enabling sparse geometric scene output. Experiments on the BOARD SLOW sequence show that the exported sparse cloud is locally consistent with EMVS reconstructions, achieving high precision at a 5 cm threshold, while also highlighting the expected limitations in density, completeness, and sensitivity to accumulated odometry noise.
Comments: 9 Pages, 4 figures, 5 tabel
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.22890 [cs.RO]
(or arXiv:2605.22890v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.22890
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Alireza Safdari Khosroshahi [view email] [v1] Thu, 21 May 2026 10:51:40 UTC (1,424 KB)
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