VOCA: Visual Odometry with Codec Awareness
Camera pose estimation from image streams is a critical component of spatial world models that integrate perception into planning and decision-making. Nearly all Visual Odometry (VO) and Simultaneous Localization and Mapping (V-SLAM) systems have focused on datasets containing raw, uncompressed videos. Many working systems instead use ubiquitous hardware units to efficiently compress and decode video streams, saving orders of magnitude in storage and bandwidth. However, this lossy compression introduces visual artifacts that hinder the performance of traditional tracking systems. We present VOCA, a causal stereo visual-odometry method that exploits codec information to improve tracking pe...
| Authors | Nouri Alexander Hilscher, Mateo de Mayo, Dominik Muhle et al. |
|---|---|
| Primary category | cs.CV |
| Published | 30 June 2026 |
| Last updated at source | 30 June 2026 |
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