# Photometric Bundle Adjustment for Vision-Based SLAM

arXiv: https://arxiv.org/abs/1608.02026 · HTML: https://mlsanity.com/paper/1608.02026/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2016.

## Built on by (most cited first, 25 of 25)

- [BARF: Bundle-Adjusting Neural Radiance Fields](https://mlsanity.com/paper/2104.06405/) (2021)
- [Pixel-Perfect Structure-from-Motion with Featuremetric Refinement](https://mlsanity.com/paper/2108.08291/) (2021)
- [ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the Wild](https://mlsanity.com/paper/2207.09137/) (2022)
- [RePOSE: Fast 6D Object Pose Refinement via Deep Texture Rendering](https://mlsanity.com/paper/2104.00633/) (2021)
- [Local-to-Global Registration for Bundle-Adjusting Neural Radiance Fields](https://mlsanity.com/paper/2211.11505/) (2022)
- [The Unreasonable Effectiveness of Pre-Trained Features for Camera Pose Refinement](https://mlsanity.com/paper/2404.10438/) (2024)
- [MonoIndoor++:Towards Better Practice of Self-Supervised Monocular Depth Estimation for Indoor Environments](https://mlsanity.com/paper/2207.08951/) (2022)
- [TrackNeRF: Bundle Adjusting NeRF from Sparse and Noisy Views via Feature Tracks](https://mlsanity.com/paper/2408.10739/) (2024)
- [Gaussian Splatting Feature Fields for Privacy-Preserving Visual Localization](https://mlsanity.com/paper/2507.23569/) (2025)
- [Self-supervised Learning of Neural Implicit Feature Fields for Camera Pose Refinement](https://mlsanity.com/paper/2406.08463/) (2024)
- [Event-based Mosaicing Bundle Adjustment](https://mlsanity.com/paper/2409.07365/) (2024)
- [NFL-BA: Near-Field Light Bundle Adjustment for SLAM in Dynamic Lighting](https://mlsanity.com/paper/2412.13176/) (2024)
- [Event-based Photometric Bundle Adjustment](https://mlsanity.com/paper/2412.14111/) (2024)
- [Can we make NeRF-based visual localization privacy-preserving?](https://mlsanity.com/paper/2508.18971/) (2025)
- [DSR: Direct Simultaneous Registration for Multiple 3D Images](https://mlsanity.com/paper/2105.10087/) (2021)
- [Photometric LiDAR and RGB-D Bundle Adjustment](https://mlsanity.com/paper/2303.16878/) (2023)
- [TD-NeRF: Novel Truncated Depth Prior for Joint Camera Pose and Neural Radiance Field Optimization](https://mlsanity.com/paper/2405.07027/) (2024)
- [From None to All: Self-Supervised 3D Reconstruction via Novel View Synthesis](https://mlsanity.com/paper/2603.27455/) (2026)
- [Online Mutual Adaptation of Deep Depth Prediction and Visual SLAM](https://mlsanity.com/paper/2111.04096/) (2021)
- [nu-DBA: Neural Implicit Dense Bundle Adjustment Enables Image-Only Driving Scene Reconstruction](https://mlsanity.com/paper/2404.18439/) (2024)
- [Physically-Based Photometric Bundle Adjustment in Non-Lambertian Environments](https://mlsanity.com/paper/2409.11854/) (2024)
- [Multi-Agent Pose Uncertainty: A Differentiable Rendering Cram'er-Rao Bound](https://mlsanity.com/paper/2510.21785/) (2025)
- [DefVINS: Visual-Inertial Odometry for Deformable Scenes](https://mlsanity.com/paper/2601.00702/) (2026)
- [Differential pose optimization in descriptor space -- Combining Geometric and Photometric Methods for Motion Estimation](https://mlsanity.com/paper/2602.14297/) (2026)
- [BA-T: An Iterative Transformer for Two-View Bundle Adjustment](https://mlsanity.com/paper/2606.03287/) (2026)

## Abstract (arXiv)

We propose a novel algorithm for the joint refinement of structure and motion parameters from image data directly without relying on fixed and known correspondences. In contrast to traditional bundle adjustment (BA) where the optimal parameters are determined by minimizing the reprojection error using tracked features, the proposed algorithm relies on maximizing the photometric consistency and estimates the correspondences implicitly. Since the proposed algorithm does not require correspondences, its application is not limited to corner-like structure; any pixel with nonvanishing gradient could be used in the estimation process. Furthermore, we demonstrate the feasibility of refining the motion and structure parameters simultaneously using the photometric in unconstrained scenes and without requiring restrictive assumptions such as planarity. The proposed algorithm is evaluated on range of challenging outdoor datasets, and it is shown to improve upon the accuracy of the state-of-the-art VSLAM methods obtained using the minimization of the reprojection error using traditional BA as well as loop closure.