# 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.