  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/
