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DIO: Dataset of 3D Mesh Models of Indoor Objects for Robotics and Computer Vision Applications

arXiv 2402.11836 ↗First posted 19 Feb 2024cs.RO cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 36 references that are in the map. First posted on arXiv 19 Feb 2024.

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14 of 36 references.

References · 14

  1. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
  2. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  3. 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,607
  4. 2022Objaverse: A Universe of Annotated 3D Objectscited by 1,522
  5. 2023ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenescited by 747
  6. 2022Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Itemscited by 601
  7. 2018PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understandingcited by 521
  8. 2021Habitat 2.0: Training Home Assistants to Rearrange their Habitatcited by 501
  9. 2017Joint 2D-3D-Semantic Data for Indoor Scene Understandingcited by 404
  10. 2021ABO: Dataset and Benchmarks for Real-World 3D Object Understandingcited by 360
  11. 20203D-FUTURE: 3D Furniture shape with TextUREcited by 306
  12. 2018Pix3D: Dataset and Methods for Single-Image 3D Shape Modelingcited by 218
2 more
  1. 2021iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Taskscited by 191
  2. 2018InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Datasetcited by 87

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