3D Multi-Angle Point Cloud Stitching Using Iterative Closest-point Stitching and K-Nearest-Neighbors

The recent focus on virtual environments and 3D object scanning has highlighted the need for accurate and efficient methods to stitch concurrent point clouds into solid three-dimensional (3D) models. To address this need, we introduce a novel iterative approach for 3D multi-angle point cloud stitching using an iterative closest point (ICP) algorithm augmented with k-nearest neighbors (kNN). With this combined algorithm, our method focuses on minimizing the error between neighboring point clouds, allowing us to easily compute the necessary transformation to combine point clouds into one model. Thus, when given concurrent point clouds captured at multiple angles of the same object, our approach provides a single accurate 3D model. We evaluated the ability of the proposed framework to stitch multiple point clouds into a solid model by stitching a segmented model and comparing the root mean squared error to a standard iterative closest-point stitching algorithm. The experiments results shows that our method provides benefits in terms of efficiency and accuracy compared to a standard approach.