Interactive 3D Reconstruction with Gaussian Splatting: Turnkey Development
Imagine: you photograph an object from 40 angles, and within an hour you get an interactive 3D scene rendering at 120 FPS—in the browser. This isn't magic, it's 3D Gaussian Splatting (3DGS). The method, introduced by Kerbl et al., combines NeRF quality with performance unattainable by classic ray marching. We build such systems turnkey: from pipeline setup to WebGL player.
We have delivered 20+ projects in 3D reconstruction, guaranteeing PSNR not below 28 dB. Budget savings up to 30% through pipeline automation.
Why 3DGS is Faster than NeRF
3DGS uses Gaussian rasterization instead of ray marching. Each Gaussian is a 3D ellipsoid with parameters (position, orientation, scale, color, opacity). Rendering reduces to projecting these ellipsoids onto the screen and alpha compositing. This yields 100–200+ FPS on consumer GPUs, while NeRF requires point sampling along rays (5–30 FPS). Quality by PSNR is comparable: 27–32 dB. The key advantage is interactive rendering without quality loss. According to 3D Gaussian Splatting for Real-Time Radiance Field Rendering, it is the first method combining rasterization speed with neural rendering quality.
| Parameter | 3D Gaussian Splatting | NeRF (Instant-NGP) |
|---|---|---|
| Render speed | 100–200+ FPS | 5–30 FPS |
| Quality (PSNR) | 27–32 dB | 26–31 dB |
| Training time | 30–60 min | 15–30 min |
| Model size | 500MB–2GB | 50–500MB |
| Scene editing | Easier (Gaussians) | Harder |
| Transparent objects | Worse | Worse |
What Problems Does 3DGS Solve in Real Projects?
The main problems we solve with 3DGS: low rendering speed of NeRF (5–30 FPS vs. 100–200 FPS), inability to edit the scene without retraining (in 3DGS, Gaussians can be moved and removed individually), and large model size (we pack a scene into 500 MB – 2 GB, loadable in seconds). Additionally, 3DGS allows distance measurement and digital twin creation directly in the browser.
What Data is Required for Quality Capture?
Successful reconstruction requires 40–120 photos with at least 80% overlap. Use a calibrated camera (iPhone, DSLR), avoid reflections and moving objects. Resolution from 12 MP. For large scenes (workshop, facade), split the object into zones with overlapping.
How We Build the 3D Reconstruction Pipeline
Stack: PyTorch, gsplat (official library), COLMAP for SfM, Hugging Face for custom modules. We use NVIDIA RTX 4090 for training — 30–60 minutes per scene. Example pipeline:
# gaussian-splatting (official implementation + nerfstudio) from nerfstudio.configs.method_configs import method_configs # 3DGS via nerfstudio # ns-train splatfacto --data ./processed # Optimization parameters config = method_configs['splatfacto'] config.pipeline.model.num_downscales = 2 # progressive training config.pipeline.model.densify_grad_thresh = 0.0002 config.pipeline.model.densify_size_thresh = 0.01 config.max_num_iterations = 30000 We adapt parameters to your scene: for glossy surfaces change densify_grad_thresh, for complex geometry adjust iteration count.
Case Study: Digital Twin of a Workshop
From our practice: one client needed to reconstruct a production workshop interior for a digital twin. Source data — 120 photos from an iPhone, capture time 20 minutes. 3DGS training on RTX 4090 — 45 minutes. Final model — 1.2 GB, rendering 150 FPS. The client could measure distances between objects directly in the browser via WebGL player. After implementation, equipment inspection time decreased by 40%.
Work Process: From Capture to WebGL Player
- Data collection: camera setup, capture with 80% overlap.
- Structure from Motion (SfM) via COLMAP to obtain sparse point cloud and camera poses.
- 3DGS training: hyperparameter tuning, GPU optimization.
- Post-processing: background removal, edge trimming, export to .ply.
- Integration: WebGL player using three-gaussian-splats, cloud upload.
Timeline and Scope
| Task | Duration |
|---|---|
| Pipeline for object capture | 3–4 weeks |
| WebGL viewer + cloud service | 6–9 weeks |
| 4D/dynamic scenes | 10–16 weeks |
Common Mistakes When Implementing 3DGS
Insufficient photos (less than 30) leads to artifacts, poor camera calibration reduces PSNR by 2–3 dB, and hyperparameter selection without scene consideration causes overfitting. We account for these nuances during preprocessing and pipeline configuration. 3DGS is the only method providing interactive rendering without quality loss.
Order a pilot project — we will train 3DGS on your data within 2 days. Get a consultation on equipment and algorithm selection. Contact us — we will evaluate your project within 2 business days.