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

by   Zexi Chen, et al.

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.


page 9

page 10

page 12

page 13

page 14

page 18

page 21

page 22


Deep Phase Correlation for End-to-End Heterogeneous Sensor Measurements Matching

The crucial step for localization is to match the current observation to...

(Just) A Spoonful of Refinements Helps the Registration Error Go Down

We tackle data-driven 3D point cloud registration. Given point correspon...

Translation Invariant Global Estimation of Heading Angle Using Sinogram of LiDAR Point Cloud

Global point cloud registration is an essential module for localization,...

Deep Global Registration

We present Deep Global Registration, a differentiable framework for pair...

Embedded Feature Similarity Optimization with Specific Parameter Initialization for 2D/3D Registration

We present a novel deep learning-based framework: Embedded Feature Simil...

Improved Fourier Mellin Invariant for Robust Rotation Estimation with Omni-cameras

Spectral methods such as the improved Fourier Mellin Invariant (iFMI) tr...

PHASER: a Robust and Correspondence-free Global Pointcloud Registration

We propose PHASER, a correspondence-free global registration of sensor-c...

Please sign up or login with your details

Forgot password? Click here to reset