March 14, 2024, 4:47 a.m. | Yanlong Li, Chamara Madarasingha, Kanchana Thilakarathna

cs.CV updates on

arXiv:2312.03298v2 Announce Type: replace
Abstract: Point cloud streaming is increasingly getting popular, evolving into the norm for interactive service delivery and the future Metaverse. However, the substantial volume of data associated with point clouds presents numerous challenges, particularly in terms of high bandwidth consumption and large storage capacity. Despite various solutions proposed thus far, with a focus on point cloud compression, upsampling, and completion, these reconstruction-related methods continue to fall short in delivering high fidelity point cloud output. As a …

abstract arxiv autoencoders bandwidth capacity challenges cloud consumption data delivery diffusion future however interactive metaverse norm popular service solutions storage streaming terms type

Senior Data Engineer

@ Displate | Warsaw

Junior Data Analyst - ESG Data

@ Institutional Shareholder Services | Mumbai

Intern Data Driven Development in Sensor Fusion for Autonomous Driving (f/m/x)

@ BMW Group | Munich, DE

Senior MLOps Engineer, Machine Learning Platform

@ GetYourGuide | Berlin

Data Engineer, Analytics

@ Meta | Menlo Park, CA

Data Engineer

@ Meta | Menlo Park, CA