r/photogrammetry

Been working on this on and off for a while and finally got the benchmarks to a point where they're not decent.

threecrate does the stuff you'd normally reach for Open3D or PCL for: kd-tree search, normal estimation, ICP/GICP/NDT registration, FPFH/SHOT features, voxel downsampling, segmentation, a handful of surface reconstruction methods, a wgpu compute path, and a basic viewer. There are Python bindings too (pip install threecrate), since a lot of the point cloud world lives in Python.

I didn't want to write the usual "rewrote it in Rust, it's faster now" post, because that isn't honestly true across the board. Here's where it actually landed against Open3D 0.19 on CPU, same machine, full-resolution TUM/KITTI/nuScenes frames:

1. Reading files: ~1.8–2.2x faster (raw float parsing; one caveat, the TUM read row isn't quite apples-to-apples and I flag that in the docs)

2. Voxel downsampling: ~1.6–1.8x faster

3. Normal estimation: 0.57–1.09x, so slower once clouds get big

4. Single-scale ICP: 0.71–0.99x, also slower on big clouds

So it's clearly ahead on I/O and down-sampling, roughly a wash on compute overall, and still behind on dense normals and ICP as the cloud grows. I know why the normals are slow: the kd-tree is pointer/Box-based and thrashes cache. Swapping in a flat, array-backed tree is the obvious fix, and it's filed as a good-first-issue if anyone wants a self-contained perf problem with a number to beat.

Full tables and the repro command are in the repo, all reproducible: https://github.com/rajgandhi1/threecrate/blob/main/docs/benchmarks.md

Repo: https://github.com/rajgandhi1/threecrate

Feedback welcome, especially if I've done something dumb in the hot loops.

We just added this to our app. Might be a huge nerd about this stuff, but I really think it's neat. You can now scrub through your photos and watch each one snap exactly into place on the 3D model <3

I tested the same industrial site with two different drone capture approaches. There's 4 stages in this comparison.

-The first reconstruction uses only Mavic 4 Pro images. It gives better overall site coverage, especially from above, but the building itself lacks close-range detail (≈1H flight time).

-The second one uses only Avata 360 images. The building has more local detail, but the surrounding area is much weaker because the flight was shorter and closer (13min flight time).

-The third version combines both drone datasets, which gave the best raw result.

-The final version uses the same combined dataset, but with longer training and cleanup to remove floaters.

The capture light wasn't stable, sunny/cloudy changes.

This is just a practical 3DGS workflow comparison for visual documentation.

You can download it here: https://www.cgbookcase.com/texture-tiling-tool

It runs on Windows and Linux. Source Code: https://codeberg.org/dorianzgraggen/texture-tiling-tool

Finally finished my first terrarium. It's a stained glass terrarium set up with bog plants. Bladderworts, sundews, pitcher plants. Ran a humidity line up through the center of the rock so mist comes out through the holes in the stone. Kind of nervous. I know carnivorous plants are real sensitive to nutrients so I picked everything to be sterile and not leach anything into the soil.

Holes look like quaders that have been cut out... memorie corruption?

OpenSfM v1.0 is here, and it's a big leap : https://github.com/OpenSfM/OpenSfM

The original OpenSfM (github.com/mapillary/opensfm), the Structure-from-Motion engine behind OpenDroneMap and WebODM, is no longer maintained.

As former maintainers, we couldn't let it go. So we went back to work, and improved it on many fronts : faster, more scalable (a long standing issue was speed) and capable of taking you from raw photos to finished GIS deliverables in a single pipeline.

We focused the v1.0 on aerial and GIS workflows :

• Much faster SfM (from Python to C++ SfM loop, GPU OpenCL matching) and support for very large scenes (randomised bundle adjuster).
• More robust SfM in general.
• A completely new dense pipeline, on GPU (OpenCL), with PatchMatch + TSDF-on-SVO + Refinement. Produces clean dense clouds quite fast, to LAS/LAZ/PLY exports. However, the mesh is crap (but it's in the works)
• On the side of the dense, a new DSM & Orthophoto pipeline, again on GPU (OpenCL). GeoTIFF export (with correct CRS projection warps)
• A revamped quality report : clear SfM metrics, GPS/GCP and checkpoint accuracy tables, and DSM/ortho previews. It can be now localized (metric/imperial, 5 languages) and also customised. PDF export.

Hopefully it'll be integrated in WebODM/ODX/ODM soon enough.

I had a blast working on this these past two months,I hope the mapping/surveying community will enjoy it.

Ha, and it runs on Linux and macOS. Windows just needs some love (update the conda-locks).

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Hi all. About a week ago I got the itch to learn something new so I crash coursed photogrammetry so I could immortalize my bonsai collection! Id also love to make a digital garden in unreal to share with my grandpa. I took on lots of his bonsai when he couldn't take care of them anymore.

Im shooting on a Canon rebel t5, using digicam to control photos, auto turntable (taking an image every 6 degrees), raw format. Importing images to reality scan and hitting each image sequence with its own ai background mask. Then aligning, hit a few control points, high def render, then texture.

It took forever to get my photos to align on 2 angles and not split into 2 components that wouldn't merge (running the ai scan individually for each angle fixed that after hours of failure).

Anyway im kinda pumped about the output on a first shoot and render ever. Im super excited to learn more, so let me know how im doing in the comments! Thanks.

Ps I have a feeling you'll tell me this is a pretty technical set of subjects, live intricate bonsai trees. I can of course get some practice on simple items around the house. I was just excited to see what I could do.

Hey everyone,

I’ve been working on pushing my raw photogrammetry reconstruction pipeline past my usual limits, moving away from decimated assets to preserve true master-level source data.

This is an oxidized bronze relief family monument. It’s a 10M+ triangle cropped open-shell mesh with a raw 16K albedo map. I processed this through a dedicated darktable pipeline to keep the overcast lighting completely flat and un-delighted, with zero procedural generation or decimation artifacts.

I'd really appreciate some feedback from this sub on the surface noise management on the weathered bronze and how the texture resolution holds up on the close-ups.

Let me know what you think of the raw output data! I will drop a link to the free source files in the comments for anyone who wants to check the UVs or mesh density.

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.