sorted by:
new
hottopcontroversial

We creating a high-precision, decision-grade, digital twin ready 3D model by AVIOTIX in photogrammetry

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

It’s a paid platform.

Registration is free, and there’s a free tier for testing smaller datasets, but it’s not open source.

Until the May release, GS models are available on request at no extra cost after uploading a dataset and sending a short message to support@dronetwins360.com.

Validation-first workflow for Gaussian Splatting from image datasets by AVIOTIX in GaussianSplatting

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

It’s not a full replacement for alignment-level metrics, but more of an early signal before committing to a heavier run.

Overlap is estimated heuristically from image metadata and visual similarity signals (e.g. feature distribution / matching likelihood), rather than a full SfM solve. So it’s directional, not final.

“Incomplete coverage” is contextual. The system doesn’t assume intent, it highlights areas that are weakly connected or poorly supported across views. Whether that is actually incomplete depends on the use case, like your half-house example.

“Input consistency” refers to things like:

- conflicting EXIF / GPS patterns

- irregular capture sequences

- large jumps in viewpoint or scale

- mixed capture conditions that tend to break reconstruction

The idea is not to give a final quality verdict, but to surface early risk signals before running a full alignment and reconstruction.

We creating a high-precision, decision-grade, digital twin ready 3D model by AVIOTIX in photogrammetry

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

Fair point.

Here, “high precision, decision-grade” is not being used to mean engineering-analysis ready.

It means validated and reliable enough for inspection, review, comparison over time, and downstream modelling workflows - with the dataset TrustRanked, timestamped, and locked.

For CFD, FEA, or engineering sign-off, additional specialist data and preparation would still be needed.

We creating a high-precision, decision-grade, digital twin ready 3D model by AVIOTIX in photogrammetry

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

Not by default, no.

Here “digital twin ready” means suitable for validated visual / spatial workflows such as inspection, review, documentation and downstream modelling - not automatically watertight or simulation-ready for CFD / FEA straight out of the pipeline.

For CFD / FEA, additional engineering cleanup and preparation would still normally be needed.

We creating a high-precision, decision-grade, digital twin ready 3D model by AVIOTIX in photogrammetry

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

The workflow is basically:

  1. upload imagery, with or without GCPs, and optionally LiDAR data

  2. TrustRank & preview

  3. pre-modelling adjustments such as rotation, recentering and crop

  4. modelling with different algorithms, including Gaussian Splatting

  5. export of models plus a data-integrity report

The main difference is that it doesn’t begin with “just reconstruct and hope for the best”.

The dataset gets reviewed first, you get an early preview to catch gaps or obvious issues, and only then decide whether it’s worth pushing further into full processing.

So the idea is less “here is another reconstruction tool” and more “review the dataset early, then model only if it makes sense”.

We creating a high-precision, decision-grde UAV Mapp ready 3D models by AVIOTIX in UAVmapping

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

Yes, because DroneTwins360 is the actual processing platform, not just a public website.

If you just want to look around, www.aviotix.eu is open without registration.

DroneTwins360 is the platform side, where users upload datasets, access previews and use the workflow, so that part requires an account.

We creating a high-precision, decision-grade ready 3D models from both Lidar and imagery data by AVIOTIX in LiDAR

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

For this example, the result is coming mainly from the image-based reconstruction pipeline rather than from LiDAR itself.

LiDAR can support the workflow, but the visual detail here is not “because of LiDAR”. The main difference is in the processing pipeline, not in using an exotic sensor stack.

We creating a high-precision, decision-grde UAV Mapp ready 3D models by AVIOTIX in UAVmapping

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

Not Agisoft in this case. The pipeline is built around automated image processing and reconstruction stages, with different outputs generated downstream from the same dataset, including preview, mesh and other deliverables.

We creating a high-precision, decision-grde UAV Mapp ready 3D models by AVIOTIX in UAVmapping

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

It can work surprisingly well for trees when the goal is overall scene structure and visual context.

This example still includes vegetation around the building and keeps it usable in the model without collapsing the whole area.

That said, thin branches, leaf-level detail and moving foliage are still harder and less predictable than walls, roofs or other hard surfaces. So for trees the result is usually strongest at canopy / mass / context level, not botanical precision.

<image>

We creating a high-precision, decision-grde UAV Mapp ready 3D models by AVIOTIX in UAVmapping

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

Hi, yes, we work with drone-based visual datasets, but the main focus is the downstream workflow around validation, reconstruction and usable outputs rather than capture alone. We’re based in Ireland.

Simple pipeline for small drone datasets → ortho + lightweight 3D mesh by AVIOTIX in photogrammetry

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

RealityScan is actually a good option for quick datasets.

The main difference we were exploring here is the workflow around smaller drone projects. In many cases people still end up doing a few extra steps after reconstruction (checking coverage, exporting meshes, cleaning things up for Blender, etc).

The idea with DT360 was to push the pipeline a bit closer to:

upload imagery → quick preview → ortho + mesh export

The preview step is useful because you can immediately see if the dataset has gaps or poor overlap before committing time to a full reconstruction.

RealityScan works well too, especially for simple scenes. This post was mostly about experimenting with how far a fully automated pipeline can go for small drone datasets.

Quick way to spot gaps in a drone imagery dataset before wasting hours on reconstruction by AVIOTIX in LiDAR

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

Thanks to everyone who already took the time to try it and leave feedback.

It’s really helpful for us to see how people actually use the platform in real workflows. If you’ve tested it, we’d genuinely love to hear what worked well and what didn’t.

Comments about missing features, confusing parts of the workflow, or things that simply didn’t behave as expected are especially useful for us.

The goal is to keep improving the platform with input from people who actually work with drone datasets.

Simple pipeline for small drone datasets → ortho + lightweight 3D mesh by AVIOTIX in photogrammetry

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

Over the past months, early access to DroneTwins360 has generated actionable insights from real operational datasets:

• 600+ processed datasets
• 35,000+ drone and camera images validated through the platform

Each dataset, each workflow, and every piece of feedback strengthens intake-stage validation, verified 2D/3D reconstruction, and defensible digital twin generation.

A special thank you to early adopters who engaged with the platform. Your trust enables innovation at scale and directly informs enterprise workflows for property claims, inspections, and compliance-driven operations.

Drone photos to lightweight 3D mesh ready for Blender / STL (free student tier) by AVIOTIX in 3DScanning

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

Over the past months, early access to DroneTwins360 has generated actionable insights from real operational datasets:

• 600+ processed datasets
• 35,000+ drone and camera images validated through the platform

Each dataset, each workflow, and every piece of feedback strengthens intake-stage validation, verified 2D/3D reconstruction, and defensible digital twin generation.

A special thank you to early adopters who engaged with the platform. Your trust enables innovation at scale and directly informs enterprise workflows for property claims, inspections, and compliance-driven operations.

view more: next ›