The three-dimensional reconstruction of real objects is an important topic in computer vision. Most of the acquisition systems are limited to reconstruct a partial view of the object obtaining in blind areas and occlusions, while in most applications a full reconstruction is required. Many authors have proposed techniques to fuse 3D surfaces by determining the motion between the different views. The first problem is related to obtaining a rough registration when such motion is not available. The second one is focused on obtaining a fine registration from an initial approximation. In this paper, a survey of the most common techniques is presented. Furthermore, a sample of the techniques has been programmed and experimental results are reported to determine the best method in the presence of noise and outliers, providing a useful guide for an interested reader including a Matlab toolbox available at the webpage of the authors.

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This paper aims to build the static-map of a dynamic scene using a mobile robot equipped with 3D sensors. The sought static-map consists of only the static scene parts, which has a vital role in scene understanding and landmark based navigation. Building static-map requires the categorization of moving and static objects. In this work, we propose a Sparse Subspace Clustering-based Motion Segmentation method that categories the static scene parts and the multiple moving objects using their 3D motion trajectories. Our motion segmentation method uses the raw trajectory data, allowing the objects to move in direct 3D space, without any projection model assumption or whatsoever. We also propose a complete pipeline for static-map building which estimates the inter-frame motion parameters by exploiting the minimal 3-point Random Sample Consensus algorithm on the feature correspondences only from the static scene parts. The proposed method has been especially designed and tested for large scene in real outdoor environments. On one hand, our 3D Motion Segmentation approach outperforms its 2D based counterparts, for extensive experiments on KITTI dataset. On the other hand, separately reconstructed static-maps and moving objects for various dynamic scenes are very satisfactory.