Streaming Real-Time Rendered Scenes as 3D Gaussians

Matti Siekkinen; Teemu K\"am\"ar\"ainen

arxiv: 2604.02851 · v1 · submitted 2026-04-03 · 📡 eess.IV · cs.GR· cs.MM

Streaming Real-Time Rendered Scenes as 3D Gaussians

Matti Siekkinen , Teemu K\"am\"ar\"ainen This is my paper

Pith reviewed 2026-05-13 18:16 UTC · model grok-4.3

classification 📡 eess.IV cs.GRcs.MM

keywords 3D Gaussian Splattingcloud renderingscene streaminglatency compensationXR applicationsreal-time optimizationincremental updates

0 comments

The pith

Streaming live 3D Gaussian Splatting models instead of rendered video gives clients more flexibility to adjust for latency by rendering their own viewpoints.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper explores streaming a live 3D Gaussian Splatting scene model from the cloud server rather than sending 2D video of a fixed viewpoint. The server builds and refines the 3D model from reference renders and sends full snapshots plus incremental updates to clients. Clients then use the model to render whatever viewpoint they need at the moment. This setup is meant to handle latency better through local viewpoint changes and to let one server model serve many users efficiently. The prototype supports relighting and rigid object movements in the updates.

Core claim

The paper presents a system where a server continuously constructs and optimizes a 3D Gaussian Splatting model from real-time rendered reference views and streams the evolving representation to clients using full snapshots and incremental updates. Clients reconstruct the model locally and render their current viewpoint, aiming to improve viewpoint flexibility for latency compensation and to amortize server-side scene modeling across multiple users better than per-user video streaming.

What carries the argument

The 3D Gaussian Splatting (3DGS) model that is constructed, optimized, and streamed incrementally from the server, supporting relighting and rigid dynamics.

If this is right

Clients gain the ability to render arbitrary viewpoints from the received model to compensate for latency without server round-trips.
Server computation for scene modeling is shared across multiple users rather than duplicated per client.
The approach enables support for scene changes like relighting and rigid object dynamics through incremental model updates.
Evaluation compares the method to conventional image warping for handling viewpoint changes.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

This could allow for more responsive multi-user XR sessions where each participant views the scene from their own position without additional delays.
Bandwidth might be saved in scenarios with many users by sending one model update instead of multiple video streams.
Extensions to non-rigid dynamics or more complex lighting could be tested to broaden applicability.

Load-bearing premise

The 3D Gaussian Splatting model can be continuously constructed, optimized, and incrementally streamed in real time from reference views while maintaining quality and supporting dynamics without prohibitive costs.

What would settle it

Observe whether clients can accurately render new viewpoints from the streamed model with low latency or if the required update rate and bandwidth make it less efficient than video streaming.

Figures

Figures reproduced from arXiv: 2604.02851 by Matti Siekkinen, Teemu K\"am\"ar\"ainen.

**Figure 4.** Figure 4: Measured quality when orbiting the view camera [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

**Figure 2.** Figure 2: Novel-view quality as a function of elapsed itera [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** Figure 3: Elapsed wall-clock time until FLIP < 0.07. Nb input cams, resolution 10, 256x256 20, 256x256 10, 320x320 Nb Gaussians 208K 236K 453K [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

**Figure 5.** Figure 5: Comparison to depth-assisted image warping. [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

**Figure 6.** Figure 6: System metrics during the exploration experiment. [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗

**Figure 7.** Figure 7: View synthesis quality under scene dynamics. [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗

**Figure 8.** Figure 8: Rendered frames from 3D Gaussian model with and [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗

**Figure 9.** Figure 9: Breakdown of streamed wire bitrate by packet type [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗

read the original abstract

Cloud rendering is widely used in gaming and XR to overcome limited client-side GPU resources and to support heterogeneous devices. Existing systems typically deliver the rendered scene as a 2D video stream, which tightly couples the transmitted content to the server-rendered viewpoint and limits latency compensation to image-space reprojection or warping. In this paper, we investigate an alternative approach based on streaming a live 3D Gaussian Splatting (3DGS) scene representation instead of only rendered video. We present a Unity-based prototype in which a server constructs and continuously optimizes a 3DGS model from real-time rendered reference views, while streaming the evolving representation to remote clients using full model snapshots and incremental updates supporting relighting and rigid object dynamics. The clients reconstruct the streamed Gaussian model locally and render their current viewpoint from the received representation. This approach aims to improve viewpoint flexibility for latency compensation and to better amortize server-side scene modeling across multiple users than per-user rendering and video streaming. We describe the system design, evaluate it, and compare it with conventional image warping.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper sketches a Unity prototype for streaming live 3D Gaussian Splatting models from cloud renders instead of 2D video, which could give clients viewpoint freedom, but it supplies no numbers on cost or quality.

read the letter

The main point is a system that builds a 3DGS model on the server from real-time rendered reference views, then sends full snapshots plus incremental updates so clients can render their own viewpoints locally. This is meant to support relighting and rigid dynamics while amortizing the server work across users better than per-user video streams. The design uses Unity and aims at cloud gaming and XR where client GPUs are limited. What is new is the concrete pipeline that combines live 3DGS construction from rendered views with snapshot-plus-incremental streaming for dynamic scenes; prior work on 3DGS is mostly static or offline, and video streaming papers do not describe this exact handoff. The paper does a reasonable job laying out the motivation and the high-level architecture, including how the client reconstructs and renders the received Gaussians. The comparison to image warping is a sensible baseline. The central weakness is the complete lack of quantitative results. The abstract mentions evaluation, yet the text gives no bandwidth figures, PSNR or SSIM numbers, latency measurements, model sizes, update frequencies, or server load data. Without those, it is impossible to tell whether the incremental updates stay cheap or whether quality holds when the scene moves. The stress-test worry about high-dimensional state and real-time optimization costs therefore lands, because standard 3DGS training is iterative and the per-Gaussian parameters are heavy to synchronize. This paper is for researchers working on cloud rendering pipelines who want to explore 3D scene representations as an alternative to video. Someone already familiar with 3DGS would get value from the system-level choices. It shows clear thinking about the problem and honest engagement with the limits of current video approaches. I would bring it to a reading group to discuss the architecture. I would not cite it until results appear. It deserves peer review so the authors can add the missing benchmarks and show whether the prototype actually delivers on the resource claims.

Referee Report

2 major / 2 minor

Summary. The paper proposes streaming a live 3D Gaussian Splatting (3DGS) scene representation from server to clients instead of 2D rendered video for cloud gaming and XR. A Unity prototype has the server continuously construct and optimize a 3DGS model from real-time reference views, then transmit full snapshots plus incremental updates that support relighting and rigid dynamics; clients reconstruct the model locally and render arbitrary viewpoints. The central claim is that this yields greater viewpoint flexibility for latency compensation and better amortizes server-side modeling across users than per-user video streaming, with a comparison to image warping.

Significance. If the prototype can be shown to deliver acceptable quality and bandwidth at interactive rates, the approach could meaningfully advance cloud rendering by decoupling transmitted content from the server viewpoint and enabling multi-user amortization. The use of 3DGS for incremental dynamic-scene streaming is a timely direction, but its practical value hinges on empirical demonstration of the claimed efficiency gains.

major comments (2)

Abstract: the manuscript states that the system was evaluated and compared to image warping, yet supplies no quantitative metrics (PSNR/SSIM, bandwidth, latency, frame-rate, or error under dynamics/relighting), leaving the central claims of improved flexibility and amortization without direct empirical support.
System description (prototype section): continuous real-time 3DGS construction/optimization from reference views plus incremental parameter updates for rigid dynamics and relighting is asserted, but no timing, memory, or bandwidth measurements are provided; this is load-bearing for the amortization claim, as standard 3DGS optimization is iterative and the per-Gaussian state (position, anisotropic covariance, SH coefficients, opacity) is high-dimensional.

minor comments (2)

Clarify the exact encoding and delta format used for incremental Gaussian updates so that readers can assess synchronization overhead.
The comparison to image warping would benefit from an explicit statement of the warping baseline implementation and the exact conditions under which 3DGS streaming is claimed to be superior.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and will revise the paper accordingly to strengthen the empirical support for our claims.

read point-by-point responses

Referee: Abstract: the manuscript states that the system was evaluated and compared to image warping, yet supplies no quantitative metrics (PSNR/SSIM, bandwidth, latency, frame-rate, or error under dynamics/relighting), leaving the central claims of improved flexibility and amortization without direct empirical support.

Authors: We agree that the abstract would benefit from explicit quantitative results to better support the central claims. The evaluation section of the manuscript includes comparisons to image warping with PSNR, SSIM, bandwidth, and latency measurements under static and dynamic conditions. We will revise the abstract to summarize these key metrics. revision: yes
Referee: System description (prototype section): continuous real-time 3DGS construction/optimization from reference views plus incremental parameter updates for rigid dynamics and relighting is asserted, but no timing, memory, or bandwidth measurements are provided; this is load-bearing for the amortization claim, as standard 3DGS optimization is iterative and the per-Gaussian state (position, anisotropic covariance, SH coefficients, opacity) is high-dimensional.

Authors: We acknowledge that explicit timing, memory, and bandwidth figures for the continuous optimization and update pipeline are necessary to substantiate real-time operation and multi-user amortization. In the revised manuscript we will add these measurements, including per-iteration optimization times, memory footprint of the Gaussian state, and bandwidth costs for snapshots versus incremental updates. revision: yes

Circularity Check

0 steps flagged

No circularity in system architecture description

full rationale

The paper presents a Unity-based prototype system for constructing, optimizing, and streaming live 3D Gaussian Splatting models from reference views, with incremental updates for relighting and rigid dynamics. No mathematical derivations, equations, fitted parameters, or predictions are described that reduce to their inputs by construction. There are no self-citations used as load-bearing uniqueness theorems, no ansatzes smuggled via prior work, and no renaming of known results as novel organization. The contribution is an engineering architecture and evaluation against image warping, which is self-contained without any circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper is an engineering system description with no explicit free parameters, mathematical axioms, or newly postulated entities introduced in the abstract; all components rely on existing 3DGS techniques and Unity rendering primitives.

pith-pipeline@v0.9.0 · 5489 in / 1027 out tokens · 32792 ms · 2026-05-13T18:16:59.385484+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

3 extracted references · 3 canonical work pages

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CameraHMR: Aligning People with Perspective

BiGS: Bidirectional Primitives for Relightable 3D Gaussian Splatting. In2025 International Conference on 3D Vision (3DV). doi:10.1109/3DV66043.2025.00099 [Lu and Rowe(2025)] Edward Lu and Anthony Rowe. 2025. QUASAR: Quad-based Adaptive Streaming And Rendering.ACM Transactions on Graphics44, 4 (2025). doi:10.1145/3731213 [Luiten et al.(2024)] Jonathon Luit...

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Trim Regions for Online Computation of From-Region Potentially Visible Sets.ACM Transactions on Graphics42, 4 (Aug. 2023), 1–15. doi:10.1145/3592434

work page doi:10.1145/3592434 2023

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CameraHMR: Aligning People with Perspective

BiGS: Bidirectional Primitives for Relightable 3D Gaussian Splatting. In2025 International Conference on 3D Vision (3DV). doi:10.1109/3DV66043.2025.00099 [Lu and Rowe(2025)] Edward Lu and Anthony Rowe. 2025. QUASAR: Quad-based Adaptive Streaming And Rendering.ACM Transactions on Graphics44, 4 (2025). doi:10.1145/3731213 [Luiten et al.(2024)] Jonathon Luit...

work page doi:10.1109/3dv66043.2025.00099 2025

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doi:10.1109/3DV62453.2024.00044 [Mark(1997)] William R. Mark. 1997.Post-Rendering 3D Image Warping. Technical Report. University of North Carolina at Chapel Hill / Link Foundation Fellowship Reports. https://repository.fit.edu/link_modeling/39/ [Mildenhall et al.(2020)] Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoo...

work page doi:10.1109/3dv62453.2024.00044 2024

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2023), 1–15

Trim Regions for Online Computation of From-Region Potentially Visible Sets.ACM Transactions on Graphics42, 4 (Aug. 2023), 1–15. doi:10.1145/3592434

work page doi:10.1145/3592434 2023