An application independent review of multimodal 3D registration methods

Saiti, Evdokia; Theoharis, Theoharis

doi:10.1016/j.cag.2020.07.012

Cited by 31 publications

(14 citation statements)

References 88 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…LiDAR odometry (LO) is the process of finding the transformation between consecutive LiDAR scans, by aligning the point cloud from the current scan to the reference scan. Reviews of the point cloud alignment methods for general purposes can be found in [ 53 , 54 , 55 , 56 , 57 ], and for autonomous driving in [ 58 , 59 ]. Based on the source of the LiDAR point cloud, both 2D [ 60 , 61 ] and 3D implementations have been shown.…”

Section: Sensors and Sensor-based Odometry Methodsmentioning

confidence: 99%

Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review

et al. 2022

View full text Add to dashboard Cite

Although Global Navigation Satellite Systems (GNSSs) generally provide adequate accuracy for outdoor localization, this is not the case for indoor environments, due to signal obstruction. Therefore, a self-contained localization scheme is beneficial under such circumstances. Modern sensors and algorithms endow moving robots with the capability to perceive their environment, and enable the deployment of novel localization schemes, such as odometry, or Simultaneous Localization and Mapping (SLAM). The former focuses on incremental localization, while the latter stores an interpretable map of the environment concurrently. In this context, this paper conducts a comprehensive review of sensor modalities, including Inertial Measurement Units (IMUs), Light Detection and Ranging (LiDAR), radio detection and ranging (radar), and cameras, as well as applications of polymers in these sensors, for indoor odometry. Furthermore, analysis and discussion of the algorithms and the fusion frameworks for pose estimation and odometry with these sensors are performed. Therefore, this paper straightens the pathway of indoor odometry from principle to application. Finally, some future prospects are discussed.

show abstract

Section: Sensors and Sensor-based Odometry Methodsmentioning

confidence: 99%

Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Our discussion is focused on methods that perform registration using geometry information. Multimodal methods that utilize geometry together with other types of information have been recently reviewed in [ST20] and are outside the scope of this survey.…”

Section: Scope and Related Workmentioning

confidence: 99%

A Survey of Non‐Rigid 3D Registration

Deng

Yao

Dyke

et al. 2022

Computer Graphics Forum

View full text Add to dashboard Cite

Non-rigid registration computes an alignment between a source surface with a target surface in a non-rigid manner. In the past decade, with the advances in 3D sensing technologies that can measure time-varying surfaces, non-rigid registration has been applied for the acquisition of deformable shapes and has a wide range of applications. This survey presents a comprehensive review of non-rigid registration methods for 3D shapes, focusing on techniques related to dynamic shape acquisition and reconstruction. In particular, we review different approaches for representing the deformation field, and the methods for computing the desired deformation. Both optimization-based and learning-based methods are covered. We also review benchmarks and datasets for evaluating non-rigid registration methods, and discuss potential future research directions.

show abstract

“…Multi-modal data may vary in the data structure, physical and anatomical principles, intensity and noise [51], which makes the registration task much more challenging. Heterogeneous pose registration tasks mainly come from medical image analysis.…”

Section: D-3d Heterogeneous Registrationmentioning

confidence: 99%

DPCN++: Differentiable Phase Correlation Network for Versatile Pose Registration

Chen¹,

Liao²,

Du³

et al. 2022

Preprint

View full text Add to dashboard Cite

Pose registration is critical in vision and robotics. This paper focuses on the challenging task of initialization-free pose registration up to 7DoF for homogeneous and heterogeneous measurements. While recent learning-based methods show promise using differentiable solvers, they either rely on heuristically defined correspondences or are prone to local minima. We present a differentiable phase correlation (DPC) solver that is globally convergent and correspondence-free. When combined with simple feature extraction networks, our general framework DPCN++ allows for versatile pose registration with arbitrary initialization. Specifically, the feature extraction networks first learn dense feature grids from a pair of homogeneous/heterogeneous measurements. These feature grids are then transformed into a translation and scale invariant spectrum representation based on Fourier transform and spherical radial aggregation, decoupling translation and scale from rotation. Next, the rotation, scale, and translation are independently and efficiently estimated in the spectrum step-by-step using the DPC solver. The entire pipeline is differentiable and trained end-to-end. We evaluate DCPN++ on a wide range of registration tasks taking different input modalities, including 2D bird's-eye view images, 3D object and scene measurements, and medical images. Experimental results demonstrate that DCPN++ outperforms both classical and learning-based baselines, especially on partially observed and heterogeneous measurements.

show abstract

An application independent review of multimodal 3D registration methods

Cited by 31 publications

References 88 publications

Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review

Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review

A Survey of Non‐Rigid 3D Registration

DPCN++: Differentiable Phase Correlation Network for Versatile Pose Registration

Contact Info

Product

Resources

About