PRISM-VO: Scale-Aware Visual Odometry Using Photometric Plenoptic Bundle Adjustment

2026-06-30

AI Summary

This work addresses the scale ambiguity inherent in monocular visual odometry and overcomes limitations of existing light-field methods—such as insufficient accuracy and complex initialization—by proposing a purely optimization-based sparse photometric visual odometry framework. The approach uniquely integrates geometric priors from light-field imaging directly into photometric bundle adjustment, jointly optimizing camera poses and inverse depths within a sliding window while fusing single-frame light-field geometric depth with multi-view temporal constraints. Notably, it achieves metric-scale reconstruction without requiring elaborate initialization and demonstrates significantly improved performance over current light-field visual odometry techniques in both indoor and outdoor environments. Its accuracy rivals that of state-of-the-art optimization- and learning-based methods, effectively mitigating trajectory drift.

Abstract

We introduce PRISM-VO, a novel pure optimization-based sparse photometric visual odometry framework for focused plenoptic cameras. The core of PRISM-VO is a novel photometric plenoptic bundle adjustment which jointly optimizes camera poses and inverse depth values of points in a sliding window. By combining geometric depth from a single plenoptic image with temporal multi-view constraints, PRISM-VO achieves accurate and drift-resilient motion estimation. Through explicit modeling of the plenoptic projection, PRISM-VO provides reliable metric-scale reconstructions, overcoming the scale ambiguity of monocular SLAM algorithms. Importantly, our approach relies solely on a single plenoptic sensor and avoids complex initialization, as depth priors are computed directly from plenoptic imaging. Experiments show that PRISM-VO outperforms the current state-of-the-art plenoptic visual odometry method on indoor and outdoor scenes. The proposed approach rivals other optimization- and learning-based methods while accurately and reliably recovering a metric scale of the scene. Project page: https://prism-vo.github.io/

Problem

• visual odometry
• scale ambiguity
• plenoptic camera
• metric-scale reconstruction
• monocular SLAM

Innovation

• photometric plenoptic bundle adjustment
• visual odometry
• plenoptic camera
• metric-scale reconstruction
• scale-aware