Mathematical model for 3D object reconstruction using OccNet (CNN)

Abstract

The input 2D image is used by the encoder to first understand the geometrical restrictions in compressed representation. Second, in the straightforward Al method, the latent representation of the input image is acquired during encoding. On the other hand, the suggested OccNet (CNN) technique computes two encoded vectors of mean and standard deviation during the encoding stage from input. The acquired encoded representation is then transformed into a three-dimensional model via the decoding process. The same decoding process is used by both of the suggested solutions. The reconstruction of a complex 3D object with colourful effects from a single 2D shot may also be the subject of future research. Unlike other methods, our representation doesn’t need a lot of memory to encode a description of the 3D output at infinite resolution. We show that our representation effectively encodes three-dimensional structure and can be deduced from a variety of inputs. Our experiments show competitive results for the difficult challenges of 3D reconstruction from single images, noisy point clouds and coarse discrete voxel grids, both qualitatively and numerically.