MobileBrick: Building LEGO for 3D Reconstruction on Mobile Devices

Abstract

High-quality 3D ground-truth shapes are critical for 3D object reconstruction evaluation. However, it is difficult to create a replica of an object in reality, and even 3D reconstructions generated by 3D scanners have artefacts that cause biases in evaluation. To address this issue, we introduce a novel multi-view RGBD dataset captured using a mobile device, which includes highly precise 3D ground-truth annotations for 153 object models featuring a diverse set of 3D structures. We obtain precise 3D ground-truth shape without relying on high-end 3D scanners by utilising LEGO models with known geometry as the 3D structures for image capture. The distinct data modality offered by high- resolution RGB images and low-resolution depth maps captured on a mobile device, when combined with precise 3D geometry annotations, presents a unique opportunity for future research on high-fidelity 3D reconstruction. Furthermore, we evaluate a range of 3D reconstruction algorithms on the proposed dataset.

Cite

Text

Li et al. "MobileBrick: Building LEGO for 3D Reconstruction on Mobile Devices." Conference on Computer Vision and Pattern Recognition, 2023. doi:10.1109/CVPR52729.2023.00474

Markdown

[Li et al. "MobileBrick: Building LEGO for 3D Reconstruction on Mobile Devices." Conference on Computer Vision and Pattern Recognition, 2023.](https://mlanthology.org/cvpr/2023/li2023cvpr-mobilebrick/) doi:10.1109/CVPR52729.2023.00474

BibTeX

@inproceedings{li2023cvpr-mobilebrick,
  title     = {{MobileBrick: Building LEGO for 3D Reconstruction on Mobile Devices}},
  author    = {Li, Kejie and Bian, Jia-Wang and Castle, Robert and Torr, Philip H.S. and Prisacariu, Victor Adrian},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2023},
  pages     = {4892-4901},
  doi       = {10.1109/CVPR52729.2023.00474},
  url       = {https://mlanthology.org/cvpr/2023/li2023cvpr-mobilebrick/}
}