Sergey Gorinsky | Searcharxiv

arXiv2025

Capture and Interact: Rapid 3D Object Acquisition and Rendering with Gaussian Splatting in Unity

Capturing and rendering three-dimensional (3D) objects in real time remain a significant challenge, yet hold substantial potential for applications in augmented reality, digital twin systems, remote collaboration and prototyping. We present an end-to-end pipeline that leverages 3D Gaussian Splatting (3D GS) to enable rapid acquisition and interactive rendering of real-world objects using a mobile device, cloud processing and a local computer. Users scan an object with a smartphone video, upload it for automated 3D reconstruction, and visualize it interactively in Unity at an average of 150 frames per second (fps) on a laptop. The system integrates mobile capture, cloud-based 3D GS and Unity rendering to support real-time telepresence. Our experiments show that the pipeline processes scans in approximately 10 minutes on a graphics processing unit (GPU) achieving real-time rendering on the laptop.

cs.GR↗

arXiv2024

An End-to-End Pipeline Perspective on Video Streaming in Best-Effort Networks: A Survey and Tutorial

Remaining a dominant force in Internet traffic, video streaming captivates end users, service providers, and researchers. This paper takes a pragmatic approach to reviewing recent advances in the field by focusing on the prevalent streaming paradigm that involves delivering long-form two-dimensional videos over the best-effort Internet with client-side adaptive bitrate (ABR) algorithms and assistance from content delivery networks (CDNs). To enhance accessibility, we supplement the survey with tutorial material. Unlike existing surveys that offer fragmented views, our work provides a holistic perspective on the entire end-to-end streaming pipeline, from video capture by a camera-equipped device to playback by the end user. Our novel perspective covers the ingestion, processing, and distribution stages of the pipeline and addresses key challenges such as video compression, upload, transcoding, ABR algorithms, CDN support, and quality of experience. We review over 200 papers and classify streaming designs by their problem-solving methodology, whether based on intuition (simple heuristics), theory (formal optimization), or machine learning (generalizable data patterns). The survey further refines these methodology-based categories and characterizes each design by additional traits such as compatible codecs and use of super resolution. We connect the reviewed research to real-world applications by discussing the practices of commercial streaming platforms. Finally, the survey highlights prominent current trends and outlines future directions in video streaming.

cs.NI↗