3D object simplification using chain code-based point clouds

This work aims to obtain a sequence of 3D point clouds associated with a 3D object that reduces the volume data and preserves the shape of the original object. The sequence contains point clouds that give different simplifications of the object, from a very fine-tuned representation to a simple and sparse one. Such a sequence is important because it satisfies different needs, from a faithful representation with a low reduction of points to a significant data reduction that only preserves the main properties of the object. We construct the sequence in the following way. We first obtain a voxelization of the original 3D object. Then, we organize the voxels by slices to get a single chain code that represents the original 3D object. The point clouds depend on the key points of the chain code. The Hausdorff distance and the average geometric error prove that the point clouds are invariant under rigid rotations and maintain the shape of the object. Our results indicate that the proposed method has an average efficiency of 60% regarding the state-of-the-art simplification methods.