Completely reconstructed three-dimensional (3D) scenes are widely used in urban planning, robot mapping, autonomous driving, and augmented reality applications. However, the occlusion of small targets in the scene or issues pertaining to the shooting equipment at a specific angle can easily result in the loss of point-cloud data. Hence, this paper proposes a method for registering and reconstructing small target objects and 3D scenes based on centroid alignment and improved fast point feature histograms (FPFHs). First, to solve the problem of different data scales for different sensors, a centroid-based transformation method was proposed to scale-align the point clouds of small target objects and the scene. Subsequently, to solve the difficulty in matching feature points between small target objects and the scene, the intrinsic shape signatures (ISS) algorithm was used to extract key points, and the proposed improved FPFH algorithm was used to complete the coarse registration of the point clouds. Finally, the bi-directional iterative closest point (ICP) algorithm was used to complete the fine registration of the point clouds and reconstruct the complete 3D scene. Experimental results show that the proposed method can solve the registration problem between the point clouds of small target objects and the scene point clouds in scenes 1?6 of the self-constructed scene dataset, thus improving the accuracy and completeness of the scene-reconstruction results. Compared with the fast global registration (FGR)+ICP and FPFH+ICP methods, the proposed method offers a higher accuracy by 77.5% on average.