You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Wu et al. [33] lift 2.5D to 3D with their 3DShapeNets approach by categorizing each voxel as free space, surface or occluded, depending on whether it is in front of, on, or behind the visible surface (i.e., the depthvalue) from the depth map.The resulting representation is a 3D binary voxel grid, which is the input to a CNN with 3D filter banks.Their method is particularly relevant in the context of this work, as they are the first to apply CNNs on a 3D representation.A similar approach is VoxNet [24], which also uses binary voxel grids and a corresponding 3D CNN architecture.The advantage of these approaches is that it can process different sources of 3D data, including LiDAR point clouds, RGB-D point clouds, and CAD models; we likewise follow this direction.

Question: What does "2.5D data" means ?
2.5D data is 2D information (image plane) plus the information about the relative depths of points, i.e., voxels - whether the points are behind or in front of the visible surface.