[R] Reconstructing 3D point clouds in an encoder-decoder manner (with equivariant encoders)

I designed some autoencoder-based method in 2D images for a paper I'm working on, and now following the feedback from previous conferences I am trying to extend my method to 3D point clouds. For this, I first have to firstly develop a 3D autoencoder (that is, from point cloud, encode to latent vector and decode to point cloud), and then of course keep building the rest of the architecture and properties of my method (autoencoder must respect some SO(3) equivariance …

autoencoder conferences decoder encoder encoder-decoder feedback images machinelearning paper