Hi,
I need handheld camera for few large scale photogrammetry projects in oil and gas. Drone with P1 would be mostly used, but I still need handheld for ground passes.
Does this camera still holds today, or is there any better option in same budget. And what is recommended lens to combine with it?
I would look at second hand market since there are some affordable options.
Thanks
I’m new in the UAV mapping “scene” and need some advice: is it worth getting Dji Terra for roof - inspecting and roof mapping? Or is there any alternative software for the beginning? (Maybe even compatible with Mac?)
I’m going to map roofs with a DJI Matrice 4E.
I hope you can help me, thx !
Hi everyone,
We've been building LocalMesh for the past few weeks, and the beta just opened.
Short version: one photo goes in, and you get a 3D Gaussian splat (~60s on a 5090, ~2min on a 4060 laptop), then a textured mesh** (UV-unwrapped, photo reprojected, AO, normal map) exported as GLB/PLY/OBJ.
Everything runs locally. No upload, no queue, no credits. Turn off your Wi-Fi, it still works.
The mesh part is the bit we're proud of. Poisson reconstruction on gaussian centers drapes a tarp over your object: a Poisson solver has to close every surface, and can never say "I can see through here".
So instead: 60 virtual cameras on a golden-angle sphere, gsplat renders depth for each, every ray carves out the empty space it travels through, and the 60 depth maps fuse into a 768³ TSDF volume. Carving instead of guessing. (gs2mesh principle, written from scratch.)
Other things you might enjoy:
- It runs on 8 GB cards. The entire conversion (splat rendering, TSDF fusion, decimation, atlas bake) stays under 7.3 GB of VRAM, so a 4060 laptop finishes the exact same job as a 5090, just slower.
- No CUDA toolkit, no Visual Studio, no Python, no admin rights on the client. We ship a fat gsplat wheel with precompiled kernels, RTX 20 to 50.
Limits, upfront: one photo means the back of the object is invented, not measured. Windows + NVIDIA 8 GB only. No Authenticode cert yet, so SmartScreen will grumble on first launch.
7 days free, no card to start, then $39 once, no subscription, because there's no cloud to pay for: https://local-mesh.com
Break it and tell us, that's what the beta is for.
Happy to answer anything about the pipeline in the comments.
This started with me wanting to train my first Gaussian Splat on my MacBook Air. It took 3.5 hours on and the thing got so hot that I filled a ZipLoc bag with cold water and balanced the laptop on top.
That was basically the start of my rabbit hole.
I gave myself a purposefully stupid goal: Make the training 1,000x faster. Not because I thought I would get there, but because I wanted to see how far AI (Fable 5) can push the whole pipeline.
To make a long story short: I started using COLMAP and Brush on the MacBook, and ended 4 weeks later with a whole custom pipeline running direct on the iPhone. No LiDAR, no cloud. The scan that took 3.5 hours initially finished in under one minute with almost identical quality.
At one point it stopped being an experiment and I decided to turn it into an iOS app: Scantic. It got released just yesterday.
The app is completely free, and I would love to hear what people think, is someone else playing with fast 3DGS on iPhone, MacOS, or mobile?
Here is the AppStore link: https://apps.apple.com/us/app/scantic/id6795293934
And here is an example scan you can view in your browser: https://scantic.app/s/GjKU2G7GFPnCdAMoC-h28A
So I've been messing around with this open-source model lately, and honestly, it's a pretty wild take on getting 3D stuff from pictures. Figured some folks here might find it interesting.
It's called SenseNova-Vision, and it's a 7B multimodal model. Apache 2.0 license, which is cool. Basically, it just sees computer vision as one big generation problem. You throw a bunch of images at it, give it some natural language instructions, and it spits out text, images, or both.
What's actually relevant for us photogrammetry nerds is that multi-view 3D reconstruction and camera pose estimation are built right in. No weird task-specific bits, no separate feature matching step. Just one model, one prompt. Pretty neat.
I tried a few things:
For multi-view reconstruction, I fed it some indoor and outdoor shots. It gives you these multi-view point maps. Honestly, the object and indoor results look pretty usable. Outdoor scenes are a bit rougher, edges get kinda soft.
Then there's camera pose estimation. It just gives you camera parameters directly from the images. I haven't actually benchmarked it against COLMAP yet, so no big claims on accuracy there. But the workflow is definitely not what I'm used to. No patch match, no CUDA dependency, no dense reconstruction pass. It's just... different.
Oh, and the same model also does segmentation, depth, keypoints, OCR. So it's one set of weights for a bunch of stuff, instead of needing a whole toolbox of specialized pipelines.
Now, for the real talk:
This is neural reconstruction, not the traditional SfM we're used to. If you absolutely need sub-millimeter accuracy, this probably isn't gonna replace COLMAP. Not right now, anyway.
It also needs some serious hardware. The web demo suggests 1x80GB GPU, and for a full benchmark, they're talking 8x80GB. Yikes.
Plus, the weights literally just dropped on July 8th, so there are definitely some rough edges to expect.
Here are the links if you wanna poke around:
HF demo: https://huggingface.co/spaces/sensenova/SenseNova-Vision
Weights: https://huggingface.co/sensenova/SenseNova-Vision-7B-MoTCode
GitHub: https://github.com/OpenSenseNova/SenseNova-Vision
Anyone else tried this or something similar? I'm curious what people think. I'd love to see someone compare neural reconstruction against COLMAP on the same image sets. That's the real test I need before I'd trust it for any serious work.
I’ve been working on an open-source frame extractor for video-to-COLMAP/3DGS workflows:
https://github.com/morishuz/frame-extractor
Instead of extracting at a fixed FPS, it tracks points using dense optical flow and selects a new frame when camera motion, tracked-point loss, or a maximum interval crosses a threshold. It also writes a CSV containing the selection and timing diagnostics.
The goal is to reduce redundant COLMAP inputs without leaving gaps that are too large to match.
I’m looking for feedback from people working with handheld, drone, dash-cam, or large-scene video:
Current limitations: this is a Python CLI rather than an end-to-end 3DGS application, and it has not yet been validated broadly on stitched 360 or multi-camera video. Reflections, texture-less surfaces, and poor capture geometry still cannot be fixed through frame selection alone.
Feedback, test results, and critical failure cases are very welcome.
​
Hey! I've been working on a project around facial approximation.
Basically, you import a 3D scan of the skull into Blender, place 24 verified craniometric landmarks (anatomical points with soft-tissue depths documented in forensic literature), and then a statistical head model — Google's GNM Head v3.0 — fits itself to those points, so the model's skin surface passes through the calculated targets.
I built a Blender addon with two side-by-side viewports: the skull on the left, where you place the landmarks, and the generated head on the right, which updates live as you work. You basically watch the face take shape as you add points.
It's open-source (Apache 2.0) and i will publish it soon.
PS. The addon was built using claude and kimi k3
Hi everyone, I’m Axel, the creator of FlaxTrax and FlaxFlow.
I’ve been working on a much more complete version of that workflow: MotionMaster 3D.
Full disclosure: this is a paid Blender add-on, so I’m marking it as a Non-free Product/Service.
The goal is simple: reduce the amount of repetitive work between “I have footage” and “I have a usable tracked 3D scene in Blender.”
MotionMaster 3D includes:
The biggest difference from FlaxTrax / FlaxFlow is that MotionMaster 3D is not just a tracker. It is built to cover the tracking + reconstruction + masking + matching workflow in one toolkit.
Demo:
Website:
https://www.motionmaster3d.com/
Product page:
https://www.motionmaster3d.com/Buy
For Reddit, I made code REDDIT25 for 25% off until 24 may
I’d also love feedback from people who do VFX or camera tracking in Blender:
What part of the tracking / reconstruction workflow still wastes the most time for you?
Open-source 3D scanning just got a level-up
I am still working on the 3d printed macro add-on for the OpenScan Mini & Classic (that might also be used in combination with a smartphone!). Therefore i am currently re-visiting some old scan object that I digitized years ago and the improved quality is stunning.
I already published an initial version of the printable files on our discord, but more details and a build-guide will follow soon.
PSA: never leave your keys unattended ;)
Hey everyone !
I've been trying some macro photogrammetry a few times now and I'm always hitting different walls I can't quite solve.
My setup:
I would like to scan an coin on a turntable with a black backdrop(in the void). Im using a Sony 90mm macro lens with ring flash (AR400). Both the lens and the flash are cross-polarized.
Im shooting at 1/60, f/20 with iso at 160.
My subject is never perfectly centered on the turntable no matter how careful I am, so as it rotates it slowly drifts out of the focus plane. At f/20 the DOF helps but it's not saving me on the wider objects. Anyone found a good way to nail centering, or is everyone just eyeballing it and accepting some throwaway frames?
I use ArUco markers on the plate for scale reference, but since they rotate with the subject, there's always some angle where the marker ends up hidden behind the object relative to the camera. Do you place multiple markers around the perimeter so at least one is always visible? Or is there a smarter placement pattern that avoids this entirely?
https://youtu.be/mMyV\_F\_9Q3E?is=enH5ReC6OvkdvOxt
This one's been the most frustrating. Since I have to physically hold the coin to scan each face, I end up with two separate scans that need to be stitched together after. The only shared geometry between the two scans is the edge/rim, and it's just too thin and low-feature to get a reliable alignment in software. Anyone dealt with this specific problem ? maybe some kind of holding jig that keeps a consistent reference visible on both scans, or a different capture order altogether?
Would love to hear how people with more experience handle this stuff. Happy to share more rig details if useful.
Good morning, I have purchased this camera to help with my terrestrial based photogrammety. Currently my setup is an iPhone with a gimble.
WebODM just doesn't like the 360 JPGs that the camera made. The error I am getting is KeyError: depthmap_resolution.
Any idea what I am doing wrong? My iPhone's images play nice with webODM. Thanks.
I have 64gb of ddr5 so I dont believe that the system memory is an issue because it has full access to the ram on the system.
I've been testing the new Insta360 X6 with the Sensori video photogrammetry app. This was quite a low light capture, but using the Bushman halo for extra illumination. There is definitely a bump in quality over the X5, although you have to look closely to see it and is less than expected given the quite noticeable jump in video quality between the two cameras.
Basically, I need some basic dimensions from a scan I found online but all I have from it are 20 pictures that rotate the model around the vertical axis (i.e horizontally spin). The focal length and camera angle are completely fixed.
I tried aligning in RealityScan but it gives me an error. I've asked some AIs for a solution and all of them tell me to either try Masking the background (some tell me to switch to Meshroom for this) or try shape-from-silhouette algorithms like Visual Hull.
Since I only care about getting basic measurements rather than anything resenbling an actual render, are those actually my best options?
Hi everyone, I'm trying to create a 3D Gaussian Splat of an interior space from only 4 existing 360° panoramas,my goal is to get a web-based interactive 3D space that I can explore, rather than just a 360° panorama.how can i do that?
Putting this up again, our next week is opening up. We run a multi camera capture setup at the Brooklyn Navy Yard and we pay people to come in and be the subject.
You stand in the volume and go through simple movements while it records. Walking, turning, sitting, reaching, picking objects up. No experience needed, and it's a decent way to see a capture stage from the inside if you've only ever been on the processing end.
Pay is 17-25 per hour, same day, right after you finish. First session runs about 2 hours, repeat work after that.
Sessions run at 4pm. Open days: Tuesday Aug 18, Wednesday Aug 19, Thursday Aug 20. Brooklyn, in person only.
Comment or DM me for the address, and ask me anything about the capture side.
Thanks to the amazing discord community (https://discord.com/invite/gpaKWPpWtG) I recently revived my search for a suitable macro lens and found a very promising candidate (~50-60€) which greatly enhances the capabilities of the arducam imx519 / hawkeye. The idea is, that the lens gets placed in front of the existing camera on either OpenScan Classic or Mini with a tiny hole (aperture) in between. The aperture increases the depth of field so that 4-5cm objects can be captured at an unseen accuracy (from such a device). I scanned the "security" key with 130 photos and no post-processing.
I am currently working on the design of the printed parts + custom ringlight (so that I can add cross-polarisation a bit later too). The files and more information is available on the openscan discord.
Hi. I’m new to draw things I just downloaded this app on my iPhone last night and I chose fluxe 1 dev 5 bit. As I was said to by gpt. I have no idea how to use it. First I add one photo but I don’t know how to add another photos so that it generates them to the prompt I want. I would really appreciate if someone could help me.
Hey all, sorry, I am extremely new to this, but essentially, I work for a recycling company and we are trying to calculate the volume of various piles of material in our facility using the existing security camera feeds we have. I talked to some more educated friends of mine and they told me what I am looking for is photogrammetry software, something like RealityScanner or Metashape, but I have no idea what I'm doing.
My questions boil down to is this the correct path for what I need to do? And if so, where do I start learning how to use these programs?
Deeply appreciate your time and any advice you can give me, thanks.
My team has been working on cracking this for a while now.
For the photogrammetry, we have different versions - COLMAP being the one we use rn, but we were also exploring Metashape and DJI Terra amongst others.
We figured how to make the full 3D map and how to plant the trajectory to 2D in coordination with the images etc but where we are getting stuck now is comparing site progress using photogrammetry and generating a progress report as per the BIM data.
We have done thorough research on our own and also tried using the full efforts of Claude and Gemini but till now, no feasible solution. We're looking to build something of our own in a short term span so the pressure is high.
Any of you facing a similar issue? Or have any suggestions regarding this?
I shoot interiors with a 360 camera, and every photogrammetry tool I tried wants pinhole images. So a panorama gets cut into 6 cube faces, and one capture position becomes 6 independent cameras that the solver doesn't know share a centre. Tracks fragment at the face borders and you throw away a lot of the sphere.
I ended up writing my own engine that keeps the sphere all the way through. One panorama stays one camera with one centre. Every residual the bundle adjustment minimises is an angle on the sphere, not a pixel on a plane. The neural front-end still sees flat patches, because that is what CNNs eat, but the geometry never leaves the sphere.
The clip is a 12-panorama apartment, cold start: no known camera positions, no priors from a previous run. Cameras appear in shooting order, and each point appears when a second camera sees it, so you are roughly watching the order the solver actually had things in. 85,615 points, about 12 minutes end to end from the raw camera files, of which ~11 minutes is the neural matching stage.
Things I cannot claim yet, before anyone asks:
- No metric scale. Everything is up to a similarity transform. I have not done a tape-measure validation, that is next.
- Per panorama it is currently about 4x slower than COLMAP on cube faces. Denser, but slower.
- Two of the twelve cameras in this scene still end up a few degrees off in rotation. Working on it.
Happy to answer anything about the spherical residual, the tiling scheme, or why sub-pixel refinement that helps ordinary cameras actively hurts on 360.
