A Dataset of 3D CAD/Mesh Models of Indoor Objects for Robotics and Computer Vision Applications
Nillan Nimal, Wenbin Li, Ronnie Clark, Sajad Saeedi
Abstract
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.
Sample Meshes
Renders of meshes from our dataset featuring high-quality geometry and textures.
Shell - NeRF Model
NeRF Models
To evaluate the compatibility of the dataset images with various formats, instant-ngp was employed to generate NeRF models.
Ralph Lauren Hat - NeRF Model
Swordfish Statue - NeRF Model
Gazebo Simulation
TurtleBot3 Waffle Pi SLAM simulation in the AWS-Robotics Small House Gazebo world with only furniture items from our dataset.
Gaussian Splatting Models
Gaussian Splatting models using images from our dataset.
Contact
If you have any questions, feel free to reach out to us at the following email: nnimal@torontomu.ca
License TBD