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

arXiv: https://arxiv.org/abs/2607.00176 · HTML: https://mlsanity.com/paper/2607.00176/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 42 references that are in the map.
- First posted on arXiv 30 Jun 2026.

## Builds on (most cited first, 15 of 15)

- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras](https://mlsanity.com/paper/2108.10869/) (2021)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [Deep Patch Visual Odometry](https://mlsanity.com/paper/2208.04726/) (2022)
- [DM-VIO: Delayed Marginalization Visual-Inertial Odometry](https://mlsanity.com/paper/2201.04114/) (2022)
- [Occlusion-Aware Cost Constructor for Light Field Depth Estimation](https://mlsanity.com/paper/2203.01576/) (2022)
- [Blur Aware Calibration of Multi-Focus Plenoptic Camera](https://mlsanity.com/paper/2004.07745/) (2020)
- [Leveraging blur information for plenoptic camera calibration](https://mlsanity.com/paper/2111.05226/) (2021)
- [LiFCal: Online Light Field Camera Calibration via Bundle Adjustment](https://mlsanity.com/paper/2408.11682/) (2024)
- [Unsupervised Learning of Depth Estimation and Visual Odometry for Sparse Light Field Cameras](https://mlsanity.com/paper/2103.11322/) (2021)
- [A Synchronized Stereo and Plenoptic Visual Odometry Dataset](https://mlsanity.com/paper/1807.09372/) (2018)
- [A Light Field Front-end for Robust SLAM in Dynamic Environments](https://mlsanity.com/paper/2012.10714/) (2020)
- [Single-Shot Metric Depth from Focused Plenoptic Cameras](https://mlsanity.com/paper/2412.02386/) (2024)
- [LiFMCR: Dataset and Benchmark for Light Field Multi-Camera Registration](https://mlsanity.com/paper/2510.13729/) (2025)

## Abstract (arXiv)

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/