Abstract: We present a technique for three-dimensional (3D) object reconstruction utilizing an ultrasonic array sensor and a variational autoencoder (VAE) within a high-interference …

Variational Autoencoders (VAEs) address some of the limitations of traditional autoencoders by introducing a probabilistic approach to encoding and decoding. The motivation behind VAEs …

Jan 25, 2021 · In a harsh remote collaboration setting, data compression techniques such as autoencoder can be utilized to obtain and transmit the data in terms of latent variables in a …

Sep 17, 2021 · In this paper, we propose voxel-based 3D object reconstruction (V3DOR) from a single 2D image for better accuracy, one using autoencoders (AE) and another using …

Mar 19, 2024 · In this study, we focus on the 3D-VAE-GAN models, a novel combination of generative adversarial networks (GANs) and variational autoencoders (VAEs) in the field of 3D …

Jan 25, 2021 · Based on the experiments on the ModelNet and Pascal3D datasets, the proposed approach shows consistently superior performance over autoencoder and variational …

In this paper, we propose a novel framework called mesh variational autoencoders (mesh VAE), which leverages the power of neural networks to explore the latent space behind deforming 3D …

This document explains how to use the 3D Variational Autoencoder (VAE) for encoding 3D assets into the point cloud-structured latent space and reconstructing them. The VAE is a …

Based on the experiments on the ModelNet and Pascal3D datasets, the proposed approach shows consistently superior performance over autoencoder and variational autoencoder up to …

Oct 27, 2023 · To tackle this problem, we propose a Scene Graph Masked Variational Auto-Encoder (SG-MVAE) framework that fully captures the relationships between objects to …