DIO: Dataset of 3D Mesh Models of Indoor Objects for Robotics and Computer Vision Applications
This provides a dataset to reduce manual effort in 3D modeling for robotics and computer vision researchers, but it is incremental as it applies existing methods to new data.
The paper tackled the problem of creating accurate 3D mesh models of indoor objects for robotics and computer vision by using photogrammetry and 3D scanning, resulting in a publicly available dataset with high-fidelity geometry and texture.
The creation of accurate virtual models of real-world objects is imperative to robotic simulations and applications such as computer vision, artificial intelligence, and machine learning. This paper documents the different methods employed for generating a database of mesh models of real-world objects. These methods address the tedious and time-intensive process of manually generating the models using CAD software. Essentially, DSLR/phone cameras were employed to acquire images of target objects. These images were processed using a photogrammetry software known as Meshroom to generate a dense surface reconstruction of the scene. The result produced by Meshroom was edited and simplified using MeshLab, a mesh-editing software to produce the final model. Based on the obtained models, this process was effective in modelling the geometry and texture of real-world objects with high fidelity. An active 3D scanner was also utilized to accelerate the process for large objects. All generated models and captured images are made available on the website of the project.