Another in my macro diorama series - this time 3 ladybugs (ladybirds here in the UK), on a cut rose stem. Interactive 3d - https://superspl.at/scene/b2461a1d

I’m trying to use Gaussian Splatting with LiDAR-generated data, but the results are honestly pretty bad compared to the same dataset but with computer generated COLMAP data.

I’m using Lumina 3D Light to generate the dataset data on an iPhone 17 Pro Max, but the exact same dataset without the COLMAP data gives a much better and actually usable result, as you can see in the video.

Am I doing something wrong with Lumina 3D Light? Has anyone else experienced this?

PS: the COLMAP data is generated in Postshot, not in RealityCapture.

You can view the Gaussian Splatting result here: https://superspl.at/scene/9d370db9

I captured a bunch of berries last week. You can download and use them as you wish.

I'm also releasing the COLMAP dataset I used for training. They have an alpha mask, the point cloud was artificially distributed. They are a tricky set to get good reflection / specular (especially the straw / blackberry). If you're working on this, please give it a go! Let me know any tricks. 😁

(Do keep in mind that this set was focus stacked)

https://www.patreon.com/posts/berries-158562920

Here is the 3D model view: https://superspl.at/scene/f5142902

Hey everyone. I've been working on a web-based Gaussian Splatting viewer for the last few months and wanted to share some findings.

Make a viewer that works on any device, including budget phones, without plugins or native apps.

- SparkJS 2.0 for rendering
- Progressive streaming via chunked RAD files
- HTTP Range requests from Cloudflare R2
- Adaptive quality system that detects GPU capabilities in real-time

Real-world results (tested on actual devices):

Scene	Desktop (RTX 3070)	Xiaomi 15 Ultra	Redmi Note 11 (€200)
Tufia (8.5M splats)	60 fps	55 fps	40 fps
XGrids Demo (12M)	60 fps	45 fps	30 fps
50M splats	60 fps	25 fps	15 fps
100M splats	55 fps	15 fps	8 fps

What I got wrong:

1. Optimized too early. Spent weeks on custom compression before realizing `build-lod --rad-chunked --max-sh=3` already hit the sweet spot. The tooling is better than I expected.

2. Ignored the data problem. Users were uploading raw 500MB PLY files and wondering why nothing loaded. Had to build the conversion step into the workflow.

3. Assumed all phones are equal. They're wildly different. The adaptive quality system exists because of this mistake.

What I learned:

- Streaming > monolithic files. Progressive loading changes the UX entirely.
- `--max-sh=3` is the sweet spot for mobile. Higher SH bands have diminishing returns on small screens.
- The web is underrated as a platform for this. More people can click a link than download an app.

Happy to answer questions about the technical details. Live demo:
https://mirador3d.com/v/penthouse-demo

One of my first not-too-bad result for such a large scale and limited images. 5 foot perch.

Colmap for tracking and gsplat for training, 50k steps. It gave me 1.2M splats, cleaned to about 500k.

Quick Gaussian Splat Editor 10-9

1. "Low Mode" Optimization (Performance)

Implemented an aggressive culling engine directly within the Shaders. Activating "LOW" mode forces the pipeline to discard splats falling below specific opacity or scale thresholds. This yields massive frame-rate improvements in heavy scenes while maintaining the integrity of the core structural geometry.

2. Per-Layer Opacity Control

Integrated individualized opacity sliders per layer. Blending calculations are processed in real-time on the GPU, enabling surgical precision when cross-fading or mixing distinct splat models.

3. Clone & Print (Experimental)

• Clone: A selection-based duplication tool designed to isolate and extract specific regions of a model into dedicated layers.
• Anchor System (A, B, C, D): Establishes a spatial reference surface/plane.
• Print Surface: A topographic deformation algorithm. It translates the source geometry toward the anchors while preserving high-frequency scan details.
• Print Sliders: Provides full control over the deformation influence and falloff gradient configuration (intensity and distance) to smoothly interpolate the edges of the deformed zone.

4. Restorer Section

A new dedicated module for data recovery and texture mapping. This feature allows users to "paint-restore" corrupted or deleted areas, or project external imagery directly onto the point cloud/splat structure.

5. Projection

• Projection: An attribute transfer system (color and position) utilizing local planar projection from the anchors onto the splat surfaces.

6. SOG Export

• New SOG (Splat Object Group) Format: A proprietary export format that encapsulates all layers, anchors, and project metadata, ensuring seamless session state recovery.

I did a quick 1min scan of myself using my Revopoint Pop 4 beta unit. I was wondering what would happen if I stared directly at the RGB camera while scanning. Can't say I'm disappointed.

Available May 20 at 1am UTC +2 Berlin

What’s new:

• 4K ARKit Image Support

Higher quality image capture for cleaner datasets and better reconstruction results.

• Tracking Recovery Guide

Helps recover AR tracking by guiding users back to the last stable scan position instead of restarting the scan.

• Experimental 20 Meter Depth Range

Extended LiDAR range for larger outdoor and indoor environments. Still experimental and being improved.

• Faster Capture + Saving

Reduced processing delays for a smoother scanning workflow.

• UI & Workflow Improvements

General usability, stability and capture workflow refinements.

• iPad Pro Support (M1 / 2021+)

Optimized support for newer iPad Pro devices.

Download:

Lumina 3D: https://3d.deluva.de/index.php?lumina_app=1

Lumina 3D Light: https://apps.apple.com/de/app/lumina-3d-light/id6767272286

Upcoming:

• RealityScan Export Support

Direct CSV + LAS export pipeline with ARKit camera poses.

• External Photogrammetry Integration

Use Lumina scans as a spatial base for DSLR and drone image alignment workflows.

• Auto Magic Capture Mode

Lumina automatically analyzes the scene, calibrates the camera for optimal scan quality and locks settings such as:

• shutter speed

• ISO

• white balance / Kelvin

• exposure behavior

This helps create more stable and photogrammetry-friendly datasets directly from mobile capture.

Long term goal:

Making high quality LiDAR + photogrammetry workflows accessible directly from mobile devices while remaining compatible with professional reconstruction pipelines.

Big thanks to everyone testing the app, sharing feedback, reporting issues and helping improve Lumina 3D

I wanted to see if you could connect two Gaussian Splat scenes through a portal you can physically walk through — no teleport, no cut, just continuous motion between two captured environments. Turns out it works pretty cleanly.

Runs entirely in the browser (WebGL2 / WebGPU), built in PlayCanvas.

Try it live: https://engine-8q342hfzj-playcanvas.vercel.app/#/gaussian-splatting/splat-portal

A few things I liked about how it came together:

• Two LOD-streamed splat scenes (a Roman parish + a skatepark), seen through a stone archway acting as the portal.
• Crossing the portal plane swaps which world you're "in" — and the same physics works symmetrically from either side, so you can walk back the way you came.
• The world you're in stays fully visible around you; the other one only shows inside the portal opening, with a proper "window" feel (no peek-around clipping).
• Subtle translucent glass inside the opening picks up specular highlights from the environment.
• The skatepark side has its own weather — procedural snow that's only visible while you're actually there (or peeking through).

Source for the example (PlayCanvas engine repo): https://github.com/playcanvas/engine/pull/8738

I just landed two new examples in the PlayCanvas engine that let you walk around *inside* a real Gaussian Splat scan — both first-person and third-person, with proper collision against the scene.

### Try them yourself

- 🚶 First-person walk: https://engine-cmbu8r47z-playcanvas.vercel.app/#/gaussian-splatting/first-person

- 🕺 Third-person with animated character (press **Q** to dance): https://engine-cmbu8r47z-playcanvas.vercel.app/#/gaussian-splatting/third-person

Controls: **WASD** to move, **Shift** to sprint, **Space** to jump, **Mouse** to look / orbit, **Scroll wheel** to zoom (third-person only).

### What's in it

- A new reusable **third-person camera controller** (`scripts/esm/third-person-controller.mjs`) modelled after the existing FPS controller. Handles:

- Orbit camera with mouse + gamepad + touch

- Wall-collision avoidance via raycast, smoothed so it doesn't pop

- Scroll-wheel zoom (smoothed, clamped)

- Configurable initial pitch, min height above character, look invert, sensitivity, damping for every axis

- Fires `speed` (0/1/2 → idle/walk/jog) and `jump` events that consumers wire into an anim state graph — no coupling to the anim system

- First-person example: capsule character + `FirstPersonController`, jumps, sprints, walks the gsplat

- Third-person example: bitmoji character, full anim state machine (idle / walk / jog / jump / dance) driven by the controller events, env-atlas IBL ambient (skybox layer disabled so the splat is the visible background), shadow catcher that follows the character on the ground via a downward raycast

### How the collision works

The "ground" is the Gaussian Splat (visual only), and physics happens against a hidden mesh GLB loaded alongside it:

- The collision mesh was **generated directly from the splat** using **splat-transform**.

- Then simplified down to **~1 MB** with https://polyforge.xyz/optimize so Ammo's BVH builder can handle it (huge meshes will OOM the WASM heap).

### Credits

Huge thanks to **zeitgeistarchivescans** for the gorgeous *Sunnyvale Heritage Park Museum* scan (CC BY 4.0):

https://superspl.at/scene/d5d397aa

### Source / PR

https://github.com/playcanvas/engine/pull/8736