Hey everyone,

I wanted to share an orthomosaic of a power substation generated using Gaussian Splatting.

Substations are packed with intricate steel framing, thin components, and crisscrossing power lines—the exact kind of geometric complexity that makes traditional photogrammetry struggle, usually leaving behind melted wires, floating noise or distortion.

3D Gaussian Splatting handles these fine, high-frequency details surprisingly well, making clean orthomosaics of extremely complex scenes like this actually viable.

Flight Specs

• Camera: DJI P1
• Images: 147 photos
• Flight Height：100 m
• Forward Overlap: 77%
• Side Overlap: 59%
• Avg. Motion Blur: 0.44 pixel
• Software: OrthoSplat

Would love to hear your thoughts!

(Disclaimer: I'm the developer behind OrthoSplat)

Hey everyone!

Over the past few weeks, I’ve been working on a comprehensive benchmark comparing mainstream orthomosaic generation software. To keep things transparent and reproducible, I spent some time sourcing high-quality public datasets. these datasets are sample data from industry-leading photogrammetry software. Out of the 9 datasets tested, 8 are publicly available for download (links are provided directly below the dataset selection panel on the site), so you can test these exact datasets in your own pipelines.

The Goal & Coverage

The main focus of this benchmark is final orthomosaic visual and structural quality. Every software was configured to run on high/maximum quality settings.

Sensor Range: Datasets cover everything from consumer drones to high-end enterprise rigs — including the DJI Phantom 4 Pro (P4P), Mavic 3 Enterprise (M3E), DJI Zenmuse P1, and 60MP high-resolution professional DSLRs.

Software Tested: Agisoft Metashape, DJI Terra, MipMap, WebODM, and OrthoSplat (a platform I developed).

Three Technological Paradigms

I wanted to evaluate how different technological approaches hold up against one another:

Classic Photogrammetry (Traditional orthorectification via DTM/DSM)

Mesh-Based Orthographic Projection (Generating a 3D mesh first, then projecting)

Gaussian Splatting (3DGS) (Generating orthomosaics directly via Gaussian Splatting)

To make side-by-side analysis effortless, I carefully aligned all output images down to pixel-level accuracy and built an interactive web platform.

https://orthoviewer.splatting3d.com/benchmark/

On the site, you can toggle between:

Slider/Wipe Mode: Drag across a single view to catch fine geometry, edge warping, or ghosting artifacts.

Dual-Screen Mode: Place two software outputs side-by-side to inspect larger spatial patterns.

As the developer behind OrthoSplat, I strongly believe Gaussian Splatting represents the future of high-detail orthomosaic generation — especially for complex vertical geometry, thin structures, and challenging textures where traditional photogrammetry often struggles. However, DSMs still require photogrammetry techniques to be generated. Gaussian Splatting and Photogrammetry are more like two mutually supportive, complementary technologies.

I hope this comparison serves as a helpful reference for anyone deciding which software to purchase or integrate into their workflow.

I’m currently writing a detailed technical blog post that breaks down the full testing workflow, along with a deep-dive comparison of the technical principles, pros, and cons of these three paradigms (Photogrammetry vs. Mesh vs. 3DGS).

Check out the site, play around with the datasets, and let me know your thoughts or feedback!

Hey everyone,

I wanted to share a recent breakthrough I had with a dataset I just processed. I took 395 nadir images captured by a M3E with 80m flight height and processed use the platform I developed (aimed to generate orthomosaics). Traditional orthomosaics always seem to struggle with certain geometric artifacts, but gaussian splatting nailed it. Here are a few things that seriously impressed me:

• Perfect Streetlight Reconstruction: Almost all the streetlights were completely restored in their exact, precise locations. Usually, thin vertical structures like poles get warped or completely omitted, but they look flawless here.
• Sharp Rooftop Edges: Complex and intricate roof structures maintained incredibly crisp and sharp edges. There's none of that usual "melting" effect you see around roof boundaries.
• Natural Vegetation: Trees and bushes actually look natural instead of looking like distorted, blocky blobs of green mesh.
• Original-Level Clarity: The overall texture clarity and sharpness of the final orthophoto are almost identical to the raw, original images.

Hey everyone,

I’ve been developing photogrammetry software for nearly a decade now. Over the years, I’ve analyzed and debugged a massive amount of 3D reconstruction and accuracy issues. What I’ve learned is that very rarely are bad results actually caused by software bugs. Most of the time, if a model's quality or accuracy doesn't meet expectations, the project was doomed during the image acquisition phase—the data was simply never going to yield a high-quality result.

With the dropping costs of drones and software, a lot more people are jumping into this field. But as many of you know, photogrammetry is highly dependent on experience and hands-on practice. It’s not something you can fully master just by watching a few YouTube tutorials. Learning from your own mistakes and failures is the fastest way to grow.

But here’s the frustrating part: when a reconstruction fails, figuring out exactly what went wrong in your workflow is rarely easy.

• Is there motion blur? (Just because an image looks sharp to the naked eye doesn't mean micro-blur isn't there).
• Were your camera shutter settings actually correct?
• Was the RTK status stable? If it dropped, which specific photos have bad RTK data?
• Is your overlap actually sufficient? (The overlap you set in your flight planning app does not always equal the actual overlap you captured, especially over changing terrain).

From flight planning to data collection to processing, there are just too many details where things can go wrong, and a mistake at any single step can compromise your final deliverable. Honestly, because getting a flawless result is so difficult, finally nailing a high-quality model is incredibly rewarding. That’s the real charm of photogrammetry.

What I'm building to help fix this

I’m currently building my own platform that generates orthomosaics using Gaussian Splatting. But while building it, I realized I wanted users to have a crystal-clear understanding of their image data quality before they even start processing.

So, I built an Image Data Quality Check feature.

I heavily optimized it to be lightning-fast, and it runs locally in your browser. If you have a laptop out in the field, you can QA your data immediately after landing. You can clearly and intuitively check:

• Drone image motion blur.
• Actual forward and side overlap rates (and how they fluctuate due to terrain).
• RTK status for every shot.
• Whether a mechanical shutter was used.

You can filter your images based on any of these parameters. No login required, and no data is uploaded to a server (you only need to log in and upload if you actually want to initiate a cloud gaussian splatting DOM task).

Note: This QA tool is primarily designed for DJI drones, as they are the ones that record all the necessary metadata required to calculate these metrics.

You can't improve what you can't measure. I’m hoping this tool can serve as a reliable benchmark for your data quality, helping you continuously professionalize your data collection and deliver more reliable, high-quality results to your clients.

Would love to hear your feedback on the QA tool.

Hi everyone,

I’m currently building a platform that generates orthomosaic using 3D Gaussian Splatting. The platform will supports both standard top-down (nadir) orthomosaic and building facade orthomosaic.

I’ve achieved a level of clarity that is significantly higher than any existing 3DGS currently on the market (this is specifically designed for generating orthomosaic). However, compared to traditional photogrammetry, the texture clarity is still looks slightly blur. I’d love to get the community's feedback on them.

Pros：

The biggest win is accurate geometry. When rendering man-made structures, there is almost zero distortion. Building edges remain straight. Corners retain their sharp right angles without any of the warping, bending, or melting effects you sometimes see in photogrammetry.

Cons:

Slightly lower texture clarity: As you can see in the attached images, the high-frequency texture detail are still softer or less crisp than what you would get from a traditional photogrammetry orthomosaic. (still working on further optimizing texture clarity)

(And no high-quality DSM from Gaussian Splatting: This is a major drawback right now. Gaussian Splatting currently cannot generate high-quality Digital Surface Models directly from the Gaussian Splatting output. Still requires photogrammetry to generate a DSM.)

My Question:

I’d love to hear from professionals working in surveying, GIS, architecture, or 3D mapping.

In your specific application fields, is accepting a slight drop in texture clarity a worthwhile trade-off to get straight building edges and undistorted geometry? Or texture clarity still the absolute priority for your workflows?

Looking forward to hearing your thoughts!