MobileBrick: Building LEGO for 3D Reconstruction on Mobile Devices
Li, Kejie, Bian, Jia-Wang, Castle, Robert, Torr, Philip H. S., Prisacariu, Victor Adrian
–arXiv.org Artificial Intelligence
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. Project page: http://code.active.vision/MobileBrick/
arXiv.org Artificial Intelligence
Mar-9-2023
- Genre:
- Research Report (0.64)
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- Technology:
- Information Technology
- Artificial Intelligence
- Machine Learning > Neural Networks
- Deep Learning (0.46)
- Vision (1.00)
- Machine Learning > Neural Networks
- Communications > Mobile (1.00)
- Hardware (1.00)
- Artificial Intelligence
- Information Technology