We propose a new method for obtaining object candidates in 3D space. Our method requires no learning, has no limitation of object properties such as compactness or symmetry, and therefore produces object candidates using a completely general approach. This method is a simple combination of Selective Search, which is a non-learning-based objectness detector working in 2D images, and a supervoxel segmentation method, which works with 3D point clouds. We made a small but non-trivial modification to supervoxel segmentation; it brings better “seeding” for supervoxels, which produces more proper object candidates as a result. Our experiments using a couple of publicly available RGB-D datasets demonstrated that our method outperformed state-of-the-art methods of generating object proposals in 2D images.
['Asako Kanezaki', 'Tatsuya Harada']
3D Selective Search for obtaining object candidates