GSStream: 3D Gaussian Splatting based Volumetric Scene Streaming System

• URL: http://arxiv.org/abs/2603.09718v1
• Date: Tue, 10 Mar 2026 14:25:00 GMT
• Title: GSStream: 3D Gaussian Splatting based Volumetric Scene Streaming System
• Abstract summary: We propose GSStream, a novel volumetric scene streaming system to support 3DGS data format.<n> GSStream integrates a collaborative viewport prediction module to better predict users' future behaviors.<n>Our proposed GSStream system outperforms existing representative volumetric streaming systems in visual quality and network usage.
• Score: 52.652341168561016
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Recently, the 3D Gaussian splatting (3DGS) technique for real-time radiance field rendering has revolutionized the field of volumetric scene representation, providing users with an immersive experience. But in return, it also poses a large amount of data volume, which is extremely bandwidth-intensive. Cutting-edge researchers have tried to introduce different approaches and construct multiple variants for 3DGS to obtain a more compact scene representation, but it is still challenging for real-time distribution. In this paper, we propose GSStream, a novel volumetric scene streaming system to support 3DGS data format. Specifically, GSStream integrates a collaborative viewport prediction module to better predict users' future behaviors by learning collaborative priors and historical priors from multiple users and users' viewport sequences and a deep reinforcement learning (DRL)-based bitrate adaptation module to tackle the state and action space variability challenge of the bitrate adaptation problem, achieving efficient volumetric scene delivery. Besides, we first build a user viewport trajectory dataset for volumetric scenes to support the training and streaming simulation. Extensive experiments prove that our proposed GSStream system outperforms existing representative volumetric scene streaming systems in visual quality and network usage. Demo video: https://youtu.be/3WEe8PN8yvA.

Related papers

• StreamSplat: Streaming Feed-Forward 3D Gaussian Splatting [13.605490241534797]
  StreamSplat is a feed-forward 3DGS framework that incrementally maintains a persistent geometry-grounded scene state.<n>Experiments show that StreamSplat remains competitive with state-of-the-art feed-forward 3DGS methods under sparse causal inputs.<n>StreamSplat scales to long input streams with 256, 512, and 1024 views where fixed-view baselines run out of memory.
  arXiv  Detail & Related papers  (2026-08-03T03:48:20Z)
• Stream3D-VLM: Online 3D Spatial Understanding with Incremental Geometry Priors [54.51965288351254]
  We present an online 3D vision-language model that enables real-time spatial understanding from streaming video.<n>Our approach adopts an autoregressive streaming control modeling based on the LLM's next-token prediction to learn when to respond.<n>To address the scarcity of streaming 3D-language data, we further develop a scalable data generation pipeline.
  arXiv  Detail & Related papers  (2026-06-05T04:16:24Z)
• Progressive Gaussian Transformer with Anisotropy-aware Sampling for Open Vocabulary Occupancy Prediction [9.952279648243058]
  We present PG-Occ, an innovative Progressive Gaussian Transformer Framework that enables open-vocabulary 3D occupancy prediction.<n>Our framework employs progressive online densification, a feed-forward strategy that gradually enhances the 3D Gaussian representation to capture fine-grained scene details.<n>Through extensive evaluations, we demonstrate that PG-Occ achieves state-of-the-art performance with a relative 14.3% mIoU improvement over the previous best performing method.
  arXiv  Detail & Related papers  (2025-10-06T12:36:07Z)
• STream3R: Scalable Sequential 3D Reconstruction with Causal Transformer [72.88105562624838]
  We present STream3R, a novel approach to 3D reconstruction that reformulates pointmap prediction as a decoder-only Transformer problem.<n>By learning geometric priors from large-scale 3D datasets, STream3R generalizes well to diverse and challenging scenarios.<n>Our results underscore the potential of causal Transformer models for online 3D perception, paving the way for real-time 3D understanding in streaming environments.
  arXiv  Detail & Related papers  (2025-08-14T17:58:05Z)
• L3GS: Layered 3D Gaussian Splats for Efficient 3D Scene Delivery [10.900680596540377]
  3D Gaussian splats (3DGS) can meet the best of both worlds, with high visual quality and efficient rendering for real-time frame rates.<n>The goal of this work is to create an efficient 3D content delivery framework that allows users to view high quality 3D scenes with 3DGS as the underlying data representation.
  arXiv  Detail & Related papers  (2025-04-07T21:23:32Z)
• V^3: Viewing Volumetric Videos on Mobiles via Streamable 2D Dynamic Gaussians [53.614560799043545]
  V3 (Viewing Volumetric Videos) is a novel approach that enables high-quality mobile rendering through the streaming of dynamic Gaussians. Our key innovation is to view dynamic 3DGS as 2D videos, facilitating the use of hardware video codecs. As the first to stream dynamic Gaussians on mobile devices, our companion player offers users an unprecedented volumetric video experience.
  arXiv  Detail & Related papers  (2024-09-20T16:54:27Z)
• SwinGS: Sliding Window Gaussian Splatting for Volumetric Video Streaming with Arbitrary Length [2.4844080708094745]
  This paper introduces SwinGS, a novel framework for training, delivering, and rendering volumetric video in a real-time streaming fashion.<n>We implement a prototype of SwinGS and demonstrate its streamability across various datasets and scenes.<n>We also develop an interactive WebGL viewer enabling real-time volumetric video playback on most devices with modern browsers.
  arXiv  Detail & Related papers  (2024-09-12T05:33:15Z)
• CityGaussian: Real-time High-quality Large-Scale Scene Rendering with Gaussians [64.6687065215713]
  CityGaussian employs a novel divide-and-conquer training approach and Level-of-Detail (LoD) strategy for efficient large-scale 3DGS training and rendering. Our approach attains state-of-theart rendering quality, enabling consistent real-time rendering of largescale scenes across vastly different scales.
  arXiv  Detail & Related papers  (2024-04-01T14:24:40Z)
• SGD: Street View Synthesis with Gaussian Splatting and Diffusion Prior [53.52396082006044]
  Current methods struggle to maintain rendering quality at the viewpoint that deviates significantly from the training viewpoints. This issue stems from the sparse training views captured by a fixed camera on a moving vehicle. We propose a novel approach that enhances the capacity of 3DGS by leveraging prior from a Diffusion Model.
  arXiv  Detail & Related papers  (2024-03-29T09:20:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.