# DSR: Direct Simultaneous Registration for Multiple 3D Images

arXiv: https://arxiv.org/abs/2105.10087 · HTML: https://mlsanity.com/paper/2105.10087/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 25 references that are in the map.
- First posted on arXiv 21 May 2021.

## Builds on (most cited first, 1 of 1)

- [Photometric Bundle Adjustment for Vision-Based SLAM](https://mlsanity.com/paper/1608.02026/) (2016)

## Built on by (most cited first, 2 of 2)

- [Ultrasound Plane Pose Regression: Assessing Generalized Pose Coordinates in the Fetal Brain](https://mlsanity.com/paper/2301.08317/) (2023)
- [Dr-BA: Separable Optimization for Direct Radar Bundle Adjustment & Localization](https://mlsanity.com/paper/2605.07041/) (2026)

## Abstract (arXiv)

This paper presents a novel algorithm named Direct Simultaneous Registration (DSR) that registers a collection of 3D images in a simultaneous fashion without specifying any reference image, feature extraction and matching, or information loss or reuse. The algorithm optimizes the global poses of local image frames by maximizing the similarity between a predefined panoramic image and local images. Although we formulate the problem as a Direct Bundle Adjustment (DBA) that jointly optimizes the poses of local frames and the intensities of the panoramic image, by investigating the independence of pose estimation from the panoramic image in the solving process, DSR is proposed to solve the poses only and proved to be able to obtain the same optimal poses as DBA. The proposed method is particularly suitable for the scenarios where distinct features are not available, such as Transesophageal Echocardiography (TEE) images. DSR is evaluated by comparing it with four widely used methods via simulated and in-vivo 3D TEE images. It is shown that the proposed method outperforms these four methods in terms of accuracy and requires much fewer computational resources than the state-of-the-art accumulated pairwise estimates (APE).