ResearchSep 2026· 12 min read

Splatting a cell tower

Taking 3D Gaussian Splatting from a room to a full BTS site, and what kept rendering on budget

Zillexa research
Final 12, Viettel AI Race 2026. The problem behind this note.
Final 12, Viettel AI Race 2026. The problem behind this note.

Most Gaussian Splatting demos live in a room: a desk, a plant, a bicycle. A BTS site is a different animal: a tall, thin structure, a lot of sky, and cameras that never quite agree on exposure.

The problem

The brief: a digital twin operators could fly through, built from a fixed set of drone and ground images. Render speed mattered as much as fidelity: a twin that takes a minute per frame is a slideshow.

We started from a standard pipeline: structure-from-motion for poses, then optimisation of a few million Gaussians.

Thin structures and empty sky are where splatting quietly gives up

Where it broke

Three failure modes showed up in the first hour: floaters in the sky, antennas dissolving into fog, and memory that grew faster than quality did.

The team debugging at their laptops
Hour nine: counting floaters by hand

Three tricks

  • Sky masking before optimisation, so the model stops inventing geometry in empty air
  • Scale-aware densification for thin parts like antennas and cables
  • Aggressive pruning on a schedule, with a hard memory budget

What's next

We’re turning the experiments into a short paper. If you work on large-scene reconstruction, let’s compare notes.

3D Gaussian SplattingDigital twinComputer vision

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