A modern web application for compressing and visualizing 3D point cloud data using advanced autoencoder technology. This project combines deep learning techniques with interactive 3D visualization to ...

In this paper, we present an end-to-end unsupervised anomaly detection framework for 3D point clouds. To the best of our knowledge, this is the first work to tackle the anomaly detection task on a ...

Masked autoencoders (MAE) have recently been introduced to 3D self-supervised pretraining for point clouds due to their great success in NLP and computer vision. Unlike MAEs used in the image domain, ...

The first one is, the LAS format of a huge 3D LiDAR point cloud dataset (Downloads, n.d), which contains the seven different point cloud images (National Lidar Dataset - Wikipedia, n.d). The second ...

utils.py: includes utils for rendering, UI, point cloud creation, etc. pointcloud_vision/ input/: contains training, validation, and test data for different environments; loss/: point cloud loss ...