Summary of S4-260186: 3DGS Software and Services

Document Overview

This contribution from Nokia provides an overview of consumer-facing 3D Gaussian Splatting (3DGS) software and services for inclusion in the draft TR for the study on 3DGS for Media (FS_3DGS-MED). The document proposes two main changes: addition of normative references and a new clause describing available 3DGS software products.

Main Technical Contributions

Addition of Normative References

The contribution proposes adding multiple new references to support the technical content:

Foundational 3DGS References:
- Kerbl et al. foundational 3DGS paper (ACM TOG 2023)
- Existing 3GPP references (TR 21.905, TR 26.928)

Image Processing and Rendering References:
- SSIM (Structural Similarity Index) - Wang et al. 2004
- GPU sorting algorithms - Satish et al.
- Alpha compositing - Porter et al. (SIGGRAPH '84)

Recent 3DGS Research (2024-2025):
- VGGT: Visual Geometry Grounded Transformer
- DepthSplat: Connecting Gaussian Splatting and Depth
- AnySplat: Feed-forward 3DGS from unconstrained views
- GS-LRM: Large Reconstruction Model for 3D Gaussian Splatting
- iLRM: Iterative Large 3D Reconstruction Model
- MetaSapiens: Real-time neural rendering with efficiency-aware pruning
- Hybrid Transparency Gaussian Splatting (HTGS)
- Sort-free Gaussian Splatting via Weighted Sum Rendering

Software and Service References:
- KIRI Engine, Niantic Scaniverse, Polycam, Luma AI, Jawset Potshot, LichtFeld Studio, SuperSplat, Gauzilla

New Clause: Software and Products (11.3)

The contribution proposes adding a comprehensive overview of consumer 3DGS software categorized by platform and capabilities:

Mobile Applications

KIRI Engine:
- Platform: iOS and Android
- Processing: Cloud-based
- Capabilities: Photogrammetry or LiDAR capture with 3DGS generation
- Export: .ply and other formats
- Limitations: Limited control over splat parameters, quality depends on capture device

Niantic Scaniverse:
- Platform: Smartphone (iOS/Android)
- Processing: Local on-device
- Pipeline: SfM for camera pose estimation + Gaussian optimization
- Export: .ply, .spz formats
- Limitations: Mobile GPU/thermal constraints limit scene size and density, no manual SH order adjustment or splat pruning

Polycam:
- Platform: Web, iOS, Android
- Processing: Cloud-based
- Capabilities: Photos/videos to Gaussian splats, also supports mesh/point cloud
- Export: .ply for splats, standard formats for meshes
- Limitations: No control over splat parameters, non-deterministic cloud processing results

Desktop Applications

Jawset Potshot:
- Platform: Windows desktop
- Processing: Local GPU-based
- Workflow: Alignment, optimization, and visualization
- Export: .ply format
- Limitations: Limited parameter tuning compared to research tools, no low-level SH coefficient control

LichtFeld Studio:
- Platform: Linux and Windows desktop
- Type: Open source
- Processing: Local GPU-based
- Input Requirements: Pre-computed SfM data (images, point clouds, camera locations)
- Features:
- 3D Unscented (3DGUT) transform for rendering
- Background Modulation for black segments
- Timelapse for intermittent quality checks
- Masking support
- Export: .ply format

Web-Based Viewers/Editors

SuperSplat and Gauzilla:
- Platform: Browser-based
- Rendering: Client-side via WebGL or WebGPU
- Capabilities: Rendering, sharing, transformations, cropping, basic filtering
- Limitations: No training or reconstruction support, lower rendering fidelity vs desktop GPU pipelines
- Use Case: Post-processing and quick visualization

Hybrid Platform

Luma AI:
- Platform: iOS and Web
- Processing: Cloud-based
- Input: Short handheld videos or image sets
- Technology: Neural scene representations rendered as Gaussian splats or hybrid neural radiance fields
- Pipeline: Pose estimation and scene normalization before splat optimization
- Limitations: No raw Gaussian parameters or SH coefficients exposed, no export capability (as of February 2026), oriented toward visualization rather than pipeline integration

Summary Table

The contribution includes a comparative table summarizing:
- Product name
- Application type (Mobile/Desktop/Web)
- Processing location (Cloud/Local)
- Export format options

This table provides a quick reference for understanding the landscape of available 3DGS tools and their capabilities.

Key Observations

The contribution demonstrates the rapid proliferation of 3DGS tools across different platforms and use cases, from mobile capture applications to desktop processing tools and web-based viewers. The tools vary significantly in:
- Processing location (cloud vs. local)
- User control over parameters
- Export capabilities
- Target use cases (capture, processing, viewing, sharing)

This overview provides important context for standardization work by documenting the current state of consumer 3DGS software ecosystem.