S4-260239 - AI Summary

[FS_3DGS_MED] Pseudo-CR on 3DGS delivery workflows for large 3DGS scenes

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Summary of 3GPP Technical Document S4-260239

Document Overview

This is a pseudo-CR to TR 26.958 v0.1.1 addressing viewport-adaptive delivery workflows for large-scale 3D Gaussian Splatting (3DGS) scenes in the context of FS_3DGS_MED study. The contribution focuses on enabling delivery of massive 3DGS environments (e.g., city-scale digital twins) to mobile devices with constrained resources.

Problem Statement

Large-scale 3DGS scenes (as defined in clause 5.4) cannot be fully loaded into mobile device memory due to:
- Bandwidth limitations
- Memory constraints
- Rendering capacity restrictions

Static delivery workflows would result in:
- Excessive latency
- Immediate resource saturation
- Inability to deliver complete scenes

Simple capability negotiation alone is insufficient for these use cases.

Main Technical Contributions

Viewport-Adaptive Workflow (Clause 9.2.3)

The document proposes a new clause 9.2.3 introducing a viewport-adaptive workflow that extends existing capability negotiation mechanisms by incorporating continuous spatial feedback.

Core Mechanism

  • Dynamic Spatial Context: UE continuously transmits 6DoF pose and Field of View (FoV) to server
  • Metadata Format: Adheres to formats defined in TR 26.928 (XR services)
  • Rendering Budget Management: Server optimizes 3DGS stream relative to user's perspective while staying within negotiated rendering budget

Spatial Optimization Strategies (Clause 9.2.3.2)

Two approaches are defined:

Tiled Environments with LOD

  • Environment partitioned into spatial tiles
  • Multiple levels of detail (LOD) per tile
  • Server selects appropriate LOD based on:
  • Proximity to user
  • Visibility within frustum
  • LOD Distribution:
  • High-density tiles (e.g., LOD 4) for viewport center
  • Lower-density tiles (e.g., LOD 1-3) for peripheral/distant areas
  • Concentrates point budget where user is looking

Unstructured Scenes

  • Real-time frustum culling, pruning, and merging
  • High point density in center of FoV
  • Aggressive simplification in peripheral zones
  • Dynamic primitive removal/merging for non-visible areas

Server-Centric Decision Workflow (Clause 9.2.3.3)

Two-Phase Approach:

Static Initialization Phase

  1. Hardware Capabilities Assessment: UE evaluates resources via system APIs or OpenXR
  2. Capability Reporting: UE transmits comprehensive capability report to server
  3. Server-Side Capability Decision: Server defines global rendering budget (max point count, SH degree) for session

Dynamic Delivery Phase

  1. Viewpoint and FoV Determination: UE calculates current 6DoF pose and camera frustum
  2. Viewpoint and FoV Information: UE sends spatial metadata to server
  3. Content Adaptation Based on FoV: Server selects visible spatial tiles and adapts content (pruning, merging, LOD selection, quantization) to fit budget and user's view
  4. Optimized 3DGS Data: Server streams adapted content payload (N points) to UE
  5. Local Adaptation: UE performs final on-device adjustments if necessary
  6. 3DGS Rendering: UE renders the scene

Key Characteristic: Server maintains control over rendering budget throughout session based on initial capability assessment.

Client-Centric Decision Workflow (Clause 9.2.3.4)

UE-Driven Approach:

Initialization Phase

  1. Hardware Assessment Analysis: UE performs internal audit of hardware capabilities
  2. Decision of Best Representation Format: UE selects optimal configuration (max point count, SH degree)
  3. 3DGS Format Request: UE requests content from server, specifying desired format parameters (point budget, SH degrees, quantization)

Delivery Phase

  1. Viewpoint and FoV Determination: UE calculates current spatial position and FoV
  2. Viewpoint and FoV Information: UE sends spatial metadata to server
  3. Content Adaptation Based on FoV: Server filters scene spatially (frustum culling/tile selection) and adapts data to match format requested in step 3
  4. Optimized 3DGS Data: Server delivers visible content conforming to requested parameters
  5. Local Adaptation: UE applies final local refinements for runtime stability
  6. 3DGS Rendering: UE renders received content

Key Characteristic: UE explicitly requests specific representation format during initialization; server's role restricted to spatial operations while adhering to UE-imposed format constraints.

Alignment with Existing Specifications

  • Builds upon capability negotiation described in clause 9.2.2
  • Aligns with viewport-dependent streaming principles from TR 26.928 (XR services)
  • Addresses use case defined in clause 5.4 (Large 3DGS scenes)

Proposal

The document proposes to agree the changes introducing clause 9.2.3 and its subclauses (9.2.3.1-9.2.3.4) to TR 26.958, including two workflow diagrams (Figures 5 and 6) and one illustration of tile/LOD selection (Figure 4).

Document Information
Source:
Tencent Cloud
Type:
pCR
For:
Agreement
Original Document:
View on 3GPP
Title: [FS_3DGS_MED] Pseudo-CR on 3DGS delivery workflows for large 3DGS scenes
Agenda item: 9.6
Agenda item description: FS_3DGS_MED (Study on 3D Gaussian splats)
Doc type: pCR
For action: Agreement
Release: Rel-20
Specification: 26.958
Version: 0.1.1
Related WIs: FS_3DGS_MED
Spec: 26.958
Contact: Julien Ricard
Uploaded: 2026-02-03T21:41:18.937000
Contact ID: 109076
Revised to: S4-260390
TDoc Status: revised
Reservation date: 03/02/2026 20:59:31
Agenda item sort order: 41