Fast Pairwise Coarse Registration Between Point Clouds of Construction Sites Using 2d Projection Based Phase Correlation

Abstract

For conducting change detection using 3D scans of a construction site, the registration between point clouds at different acquisition times is normally necessary. However, due to the complexity of constructing areas, the automatic registration of temporal scans is a challenging problem. In this work, we propose a fast and maker-free method for coarse registration between point clouds by converting the 3D matching problem into a 2D correlation problem, taking the special properties of building structures into consideration. Our proposed method consists of two major steps: the conversion from 3D points to 2D image data and the estimation of transformation parameters between 2D images in the frequency domain. In the conversion step, the point cloud of each scan is projected into a 2D grey image, by which the ground footprint of the point cloud is obtained. In the following step, we represent the 2D image in frequency-domain and estimate the horizontal transformation parameters by using Fourier-Mellin transformation. A real application is performed to validate the feasibility and effectiveness of our workflow using photogrammetric point clouds of a construction site in two different acquisition time. Regarding the real application of coarse registration of point clouds, our proposed method can achieve a registration error of less than 1 degree and more efficient than the classical baseline methods for the fast orientation between scans.

Robust global registration of point clouds by closed-form solution in the frequency domain

“…In a recent work of (Dong et al, 2018), global features were used for fast orientation of multi-scan unordered point clouds. In our previous work (Huang et al, 2019), 3D point clouds of a highly complicated scenario were projected into 1D histograms and 2D images for achieving registration in low-dimensional spaces. These projected histograms and images were also a global expression of original point clouds.…”

“…In the proposed method, point clouds are aligned with a transformation of seven degrees of freedom (DoFs) using global features generated in the frequency domain. Global features are deemed to be less easily influenced by low-overlapping issues and unevenly distributed point densities than features constructed based on local context (Huang et al, 2019(Huang et al, , 2020a. Besides, high-frequency components which indicates noise and outliers in the 3D signals can be eliminated by representing 3D points using discrete signals and transforming them to the frequency domain.…”

Ridf: A Robust Rotation-Invariant Descriptor for 3d Point Cloud Registration in the Frequency Domain

“…In 2D image matching, methods based on Fourier transforms are commonly utilized, such as phase correlation (Nagashima et al, 2006). In (Huang et al, 2019), the strategy of Fourier-based 2D image matching was successfully applied to 3D point cloud registration. In (Liu et al, 2014), a 3D rotation-invariant descriptor was proposed and tested in the task of shape retrieval.…”

Robust Coarse-to-Fine Registration Scheme for Mobile Laser Scanner Point Clouds Using Multiscale Eigenvalue Statistic-Based Descriptor

“…Point clouds obtained with modern three-dimensional (3D) sensors, such as mobile laser scanner (MLS), have played an important role in civil and transportation engineering [ 1 , 2 , 3 ], forest structure monitoring [ 4 , 5 ], and spatial deformation monitoring [ 6 , 7 ]. However, due to errors in the calibration and positioning of sensors, MLS point clouds obtained from different frames or periods suffer deviations, several tens of centimeters and even to meters [ 8 ].…”