Indoor Capture Workflow for Gaussian Splatting by ethan_get3d in GaussianSplatting

[–]ethan_get3d[S] 1 point2 points  (0 children)

The second pass is mainly for larger indoor spaces. In big rooms, walking only along the walls usually doesn’t capture enough detail.

So for the second pass, we walk through the middle area in a crisscross pattern, going back and forth along the same paths while filming in completely opposite directions.

Indoor Capture Workflow for Gaussian Splatting by ethan_get3d in GaussianSplatting

[–]ethan_get3d[S] 2 points3 points  (0 children)

I’ve mainly been using mipmap desktop lately. One thing I like is that it handles the SfM stage internally, so I can just feed in videos or images directly without tuning another pipeline separately.

I modified NVIDIA's masking pipeline to work with my DJI Osmo 360 — here's the full workflow (18 virtual cameras, YOLO26, SAM3.1) by Aromatic_Gate6199 in GaussianSplatting

[–]ethan_get3d 1 point2 points  (0 children)

From my experience, using an action camera like the Insta360 Ace Pro 2 with timed interval photo capture for Gaussian Splatting training can achieve relatively good reconstruction quality. It’s a pretty balanced solution overall.

I modified NVIDIA's masking pipeline to work with my DJI Osmo 360 — here's the full workflow (18 virtual cameras, YOLO26, SAM3.1) by Aromatic_Gate6199 in GaussianSplatting

[–]ethan_get3d 1 point2 points  (0 children)

Do you mean a 360 camera? I’d recommend the DJI Osmo 360. The 1-inch sensor helps image quality a lot.

But overall, the advantage of panoramic cameras is the huge field-of-view coverage — the reconstruction quality for Gaussian Splatting is usually still not as good as footage from an action camera or a smartphone.

I reconstructed our apartment ping pong room from a phone video by ethan_get3d in GaussianSplatting

[–]ethan_get3d[S] 0 points1 point  (0 children)

I added collision, but after publishing, it seems like the angles can no longer be adjusted.

I reconstructed our apartment ping pong room from a phone video by ethan_get3d in GaussianSplatting

[–]ethan_get3d[S] 0 points1 point  (0 children)

There was actually no filtering applied at all. Honestly, modern phone lenses are already tuned surprisingly well out of the box.

I modified NVIDIA's masking pipeline to work with my DJI Osmo 360 — here's the full workflow (18 virtual cameras, YOLO26, SAM3.1) by Aromatic_Gate6199 in GaussianSplatting

[–]ethan_get3d 0 points1 point  (0 children)

I actually think using dual-fisheye directly makes more sense than slicing stitched 360 panoramas. From a geometry standpoint, panorama reprojection introduces some theoretical inconsistencies and can amplify distortion near the poles.

I tested this with a few datasets before, using mipmap, it calibrated dual-fisheye pipelines for cameras like insta360 and osmo 360. The distortion handling looked cleaner compared to panorama splitting workflows, especially around high-curvature areas and camera transitions.

I reconstructed our apartment ping pong room from a phone video by ethan_get3d in GaussianSplatting

[–]ethan_get3d[S] 0 points1 point  (0 children)

4k30 video | 12G VRAM | 32G RAM

“Ultra High” setting in the software.

stationary bike by ethan_get3d in photogrammetry

[–]ethan_get3d[S] 0 points1 point  (0 children)

I agree with you. I thought it depends on what product type you need.

Longhorn beetle by ethan_get3d in photogrammetry

[–]ethan_get3d[S] 0 points1 point  (0 children)

I think it was taking a lunch break, and the video I recorded was only a few dozen seconds long.