Here's a rewritten and improved version of the original content in English, with added details to make it more natural and engaging, while keeping the core message intact. The final text is over 500 characters: --- One of the most common questions we get is: **"How accurate is the 3D model I created using Elios 2 visual data?"** The answer isn’t straightforward because the accuracy of your 3D model depends on the quality of the data you used to build it. However, there is a general rule that applies to any 3D model made from visual data. Theoretically, the maximum accuracy achievable with Elios 2 is three times the Ground Sampling Distance (GSD). GSD refers to the size of each pixel as it appears on the surface of the model. Since the Elios 2 uses a 4K camera, the maximum GSD you can achieve is 0.25mm per pixel. This means the theoretical maximum accuracy of your model could be around 0.75mm. But for simplicity, we often round this up to **1mm**. This level of precision can be achieved when using reference points close to the objects being measured. A video demonstrates how this works, showing how scale constraints help ensure accuracy during the modeling process. In the video, the experiment was conducted under ideal conditions—no backlight, non-reflective surfaces, and plenty of texture. The area modeled was small (about 0.5 m²), which allowed for precise measurements of objects smaller than 5cm. Two scale constraints and one orientation constraint were used to maintain accuracy, and the measurements were taken very near those references. It’s important to note that the model wasn’t tied to any specific coordinate system, so the results reflect relative accuracy rather than absolute positioning. While 1mm accuracy is possible, it’s not always necessary or practical for every project. Your desired level of accuracy should be planned based on what you need the model for. If you’re measuring tiny components or checking distances between parts, you’ll need to fly closer and collect high-quality data. On the other hand, if you just need a general idea of where things are, you don’t need to go to the same level of detail. To ensure accuracy in your 3D model, here are some key considerations: - **More reference points are needed for larger areas.** The more space you cover, the more scale constraints you should include. - **Distance matters.** The closer you are to the object, the better the GSD, and thus the higher the accuracy. - **Lighting plays a role.** Poor lighting or dust can reduce image quality, affecting the final model. - **Smooth drone flight is essential.** Blurry images due to shaky movement will lower the model’s accuracy. - **Reflective surfaces are harder to map.** They offer fewer features, making it harder for software to match images. Ground Sampling Distance (GSD) is a critical factor. It represents the real-world distance between two adjacent pixels in an image. A lower GSD means higher resolution and more detail. For example, a GSD of 0.18 mm/px at 30 cm away allows for extremely detailed models. Fisheye lenses, like the one on the Elios 2, can affect GSD depending on where the object is located in the frame. Objects in the center have a smaller GSD, while those in the corners have a larger one. Accuracy requirements vary by use case. For land surveys, less precision might be acceptable, but for indoor inspections, you may need to know exact distances down to millimeters. For instance, if you're inspecting a refractory lining in a duct, knowing the exact location of a defect is crucial for safety and maintenance planning. If you want to dive deeper into photogrammetry with the Elios 2, check out these resources: - [Webinar] Indoor 3D Modeling Use Cases: Photogrammetry in Action - [Webinar] Indoor 3D Modeling: Applications & Implications - [Article] Why use photogrammetry to build 3D models of indoor spaces - [Article] Building 3D models with Elios 2: How to acquire appropriate data for photogrammetry - [Article] Building 3D models with Elios 2: Processing data with a photogrammetry software Whether you're mapping large areas or capturing fine details, understanding the factors that influence accuracy will help you get the best results from your 3D models.

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