I’m currently trying to work on a project which I’m working with point clouds that were created with a LiDAR scanner. I know that occupancy grids seems to be popular and straightforwards to work with, but they require an extremely large input, since every possible point has to be either marked as filled or empty. However, I could also use depth map, but since it is a flat image, where the grayscale color represents, but this isn’t true 3d data, more like 2.5d.  First, would it be useful to convert a 2d input layer that takes a depth map, to a 3d convolution, and would this help solve my issue of not having true 3d data? Second, how would this be implemented, and could someone please link an example of how to upscale a 2d convolution to a 3d convolution? Third, is there any other efficient method that can be used to convert a 3d point cloud into a true 3d format, that can be fed into a 3d convolutional neural network?