May 24, 2020 · This document summarizes deep learning techniques for 3D point clouds. It discusses methods for 3D shape classification, object detection and tracking, and …

Our proposal explores the properties of 1D-convolutions, used in state-of-the art point cloud autoencoder architectures to handle the input data, which leads to an intuitive interpretation of …

In this work, we present a novel scheme of masked autoencoders for point cloud self-supervised learning, termed as Point-MAE. Our Point-MAE is neat and efficient, with minimal …

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 …

Our work presents a parameter-efficient prompt tuning method, named PPT, to adapt a large multi-modal model for 3D point cloud understanding. Existing strategies are quite expensive in …

Zhou et al. VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection. CVPR 2018.

A 3D Laser Scanning systems will quickly capture millions of points to be used to create Polygon Models, IGES / NURBS Surfaces, or for 3D Inspection against an existing CAD model. 7 …

In this paper, we put forward a novel point cloud generation framework called PointIVAE, which adopts VAE based framework to construct local relations and enhance generating capability. …

Jan 3, 2022 · Abstract: This paper advocates the use of implicit surface representation in autoencoder-based self-supervised 3D representation learning. The most popular and …

Our proposal explores the properties of 1D-convolutions, used in state-of-the art point cloud autoencoder architectures to handle the input data, which leads to an intuitive interpretation of …