Sphere detection in point clouds is an important task in 3D computer vision with various applications such as reverse engineering, medical imaging, Terrestrial Laser Scans (TLS) alignment, and so on. So far, several approaches have been proposed to detect spheres in point clouds. However, conventional methods are inefficient and inaccurate because they depend on random sampling, point-wise voting or normal vectors estimation to generate hypothetical spheres. To overcome these drawbacks, we propose a novel algorithm that employs sliding voxels and Hough voting to robustly and efficiently detect spheres in unorganized point clouds. The proposed method can analyze all the points contained in point clouds without deteriorating its efficiency and accuracy in contrast to conventional methods. Through experiments, we found that the proposed method can drastically reduce the processing time and achieve more accurate and robust performance in severer conditions than conventional methods.