A Jupyter notebook containing a PyTorch implementation of Point Cloud Autoencoder inspired from "Learning Representations and Generative Models For 3D Point Clouds". Encoder is a …

In this article, I present a Torch implementation of a PointNet auto-encoder — a network allowing to reconstruct point clouds through a lower-dimensional bottleneck. As loss during training, I …

I'm creating a graph-based autoencoder for point-clouds. The original point-cloud's shape is [3, 1024] - 1024 points, each of which has 3 coordinates. A point-cloud is turned into an …

Given a continuous 3D shape, there are infinitely many ways to sample a point cloud. The proposed Implicit AutoEncoder (IAE) learns a latent represen-tation of the true 3D geometry …

This is a framework for running common deep learning models for point cloud analysis tasks against classic benchmark. It heavily relies on Pytorch Geometric and Facebook Hydra. The …

In this tutorial, we will take a closer look at autoencoders (AE). Autoencoders are trained on encoding input data such as images into a smaller feature vector, and afterward, reconstruct it …

In this project, the problem of generating point clouds is examined using VAEs. The proposed models use per-mutation invariant encoder and fully connected layers as decoders. Different …

Apr 29, 2025 · In this paper, we propose a novel deep learning framework for generating point clouds from WiFi CSI data. Our approach leverages a two-stage autoencoder architecture. …

This is an implementation for FoldingNet in PyTorch. FoldingNet is a autoencoder for point cloud. As for the details of the paper, please reference on arXiv.

Given a point cloud dataset con-taining objects with various genera, or scenes with multiple objects, we propose an autoencoder, TearingNet, which tack-les the challenging task of …