Accuracy Assessment of an Image-Based Cloud-Based Indoor Mapping Platform as an Alternative for Desktop Applications

The generation of 3D spatial datasets, especially in indoor space, has continually been a challenge in developing spatial applications that are aimed for the realistic representation of the real world. Despite their potential, the creation of 3D spatial data remains a challenge, particularly in terms of cost and complexity associated with traditional methods. While this method proves to be a reliable and accurate method of creating such data, this approach entails challenges in economic, computational, and human resource aspects. Alternatively, omnidirectional images have emerged as cost-effective alternatives for representing 3D spaces, offering a comprehensive field of view and facilitating the generation of structure and appearance representations through mesh-based 3D maps. This study evaluates the geometric accuracy of a cloud-based image-based mapping platform for generating 3D representations of indoor spaces, aiming to assess its suitability as an alternative to desktop-based platforms. We obtained 30 sample lines and compare respective measurements from each of the generated meshes to a ground truth value. Results show that while there are no significant differences in the mean errors, the mesh generated from a cloud-based platform produced minimal errors. This demonstrates the potential of this platform for generating 3D representations of indoor space, acceptable in both geometric accuracy and visualization capabilities.