Abstract
Pairwise rigid registration aims to find the rigid transformation that best registers two surfaces represented by point clouds. This work presents a comparison between seven algorithms, with different strategies to tackle rigid registration tasks. We focus on the frame-to-frame problem, in which the point clouds are extracted from a video sequence with depth information generating partial overlapping 3D data. We use both point clouds and RGB-D video streams in the experimental results. The former is considered under different viewpoints with the addition of a case-study simulating missing data. Since the ground truth rotation is provided, we discuss four different metrics to measure the rotation error in this case. Among the seven considered techniques, the Sparse ICP and Sparse ICP-CTSF outperform the other five ones in the point cloud registration experiments without considering incomplete data. However, the evaluation facing missing data indicates sensitivity for these methods against this problem and favors ICP-CTSF in such situations. In the tests with video sequences, the depth information is segmented in the first step, to get the target region. Next, the registration algorithms are applied and the average root mean squared error, rotation and translation errors are computed. Besides, we analyze the robustness of the algorithms against spatial and temporal sampling rates. We conclude from the experiments using a depth video sequences that ICP-CTSF is the best technique for frame-to-frame registration.
Highlights
Surface registration is a common computer vision problem, with applications in computer graphics, robotics, quality inspection, photogrammetry, augmented reality, pose estimation, among others [1]
In order to limit the scope of the problem, and avoid a combinatorial explosion in the number of possibilities to test, we focus on rigid transformation techniques that fulfill at least one of the following requirements: (a) Incorporate local geometric features to enhance the quality of the matching step; (b) Estimate the transformation using a distance different from the Euclidean one; (c) Perform registration without correspondence
WORKS In this paper we consider the frame-to-frame registration problem, in which the point clouds are extracted from a video sequence with depth information
Summary
Surface registration is a common computer vision problem, with applications in computer graphics, robotics, quality inspection, photogrammetry, augmented reality, pose estimation, among others [1]. Rigid registration is a sub-problem, dealing only with sets that differ by a rigid motion. In this problem, given two point clouds, named source set P = {pi|pi = (pix, piy, piz)} and target set Q = {qj|qj = (qjx, qjy, qjz)}, we need to find a motion transformation ψ, composed by a rotation R and a translation t, that applied to P best aligns both clouds (ψ(P ) ≈ Q), according to a distance metric. The classical and most cited algorithm in the literature to rigid registration is the Iterative Closest Point (ICP) [2]. This algorithm takes as input the point clouds P and Q, and consists of the iteration of two major steps: matching between the point clouds and transformation estimation. These two steps are iterated until a termination criterion is satisfied
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