3D Local feature description is an active and fundamental task in 3D computer vision. However, most of the existing descriptors fail to simultaneously achieve satisfactory performance among descriptiveness, robustness, efficiency, and compactness. To address these limitations, we first propose a real-valued descriptor named Rotational Voxels Statistics Histogram (RoVo), which exploits the novel 3D multi-pose processing mechanism proposed in this paper to calculate the 3D voxel density distribution in different 3D poses. Moreover, through well-designed binary encoding algorithms, we conduct the seamless extension of the real-valued RoVo descriptor to three binary representations that have different performance characteristics. Extensive evaluation experiments validate the superiority of the real-valued and three binary RoVo descriptors concerning descriptiveness, robustness, and efficiency. Furthermore, the three binary RoVo descriptors extend the performance of high compactness. Lastly, we perform the experiments of 3D scene registration and 3D object recognition to intuitively present the effectiveness of the four proposed RoVo descriptors.