DSR: Direct Simultaneous Registration for Multiple 3D Images

TL;DR

The paper introduces DSR, a novel algorithm for simultaneous registration of multiple 3D images that does not rely on feature extraction, and demonstrates improved accuracy and efficiency over existing methods, especially in featureless scenarios.

Contribution

The paper proposes DSR, a direct, featureless, simultaneous registration algorithm that optimizes global poses without reference images or feature matching, outperforming existing methods.

Findings

DSR outperforms four widely used methods in accuracy.

DSR requires fewer computational resources.

DSR is effective for featureless 3D images like TEE.

Abstract

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).