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u/ethan_get3d

r/GaussianSplatting r/photogrammetry
▲ 197 r/photogrammetry+1 crossposts

Garage - High Detail

Gaussian viewer: https://superspl.at/scene/8f4d8e05

Source images: Publicly available dataset from Kaggle: Garage Dataset by Simon Bethke

Gaussian training software: MipMap Desktop (Align and Train workflow) using the Ultra High Quality preset.

I think this capture methodology is worth studying, the source photos are of exceptionally high quality. After the alignment stage is completed, you can inspect the recovered camera positions and orientations directly.

u/ethan_get3d — 12 days ago
▲ 126 r/photogrammetry+1 crossposts

Photos captured using a circular flight path with the DJI M4E, totaling 195 images.

The gaussian splatting model was trained in mipmap desktop. I believe the circular flight path is an excellent data acquisition strategy for gaussian splatting—it requires fewer images while producing better.

You can view the model here and download it freely: https://superspl.at/scene/3dd2b75e

A drone circular flight path (orbit flight / circling trajectory) is a UAV capture method where the drone flies around a target object or area in a smooth circle while continuously pointing the camera toward the center.

u/ethan_get3d — 15 days ago
▲ 80 r/GaussianSplatting

I turned a 3D model into a Gaussian Splat just by screen recording it

Here is my work: https://superspl.at/scene/ffbefb20

Many people think Gaussian Splatting always requires a real-world photo dataset.

But recently I experimented with a different workflow:

Instead of capturing a real object with a camera, I simply loaded a 3D model, rotated around or moved along a path in a indoor room on screen, recorded a video, extracted frames, and trained a Gaussian Splat from those images.

The pipeline is surprisingly simple:

  1. Load the model in a 3D viewer (Blender, Sketchfab, Unreal Engine, etc.)
  2. Slowly orbit around the object while recording the screen
  3. Extract video frames
  4. Run SfM + Gaussian Splatting training
  5. Export and view the resulting GS model

Because the virtual camera trajectory is smooth and the rendering is noise-free, the reconstruction can be extremely stable.

Some interesting observations:

  • No motion blur
  • No exposure variation
  • No rolling shutter artifacts
  • Perfect texture consistency
  • Easy to generate thousands of viewpoints

In some cases the resulting Gaussian model looks almost identical to the original rendered mesh.

I'm curious:

Has anyone here tried converting existing 3D assets into Gaussian Splats this way?

u/ethan_get3d — 1 month ago
▲ 42 r/photogrammetry+1 crossposts

I reconstructed our apartment ping pong room from a phone video

  1. 6 minites iphone14 4k video.

  2. Walked around the corner and captured the opposite side from different angles, while also tilting the camera to capture the ceiling and floor

  3. Trained with mipmap.

view the model here https://superspl.at/scene/0d9e613b, looking forward to your feedback!

u/ethan_get3d — 1 month ago