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/

翻译：高质量的三维真实标注数据对于三维物体重建评估至关重要。然而，在现实中复制真实物体存在困难，即便是三维扫描仪生成的重建结果也常带有伪影，导致评估偏差。为解决这一问题，我们提出一个基于移动设备采集的新型多视角RGBD数据集，其中包含153个具有多样化三维结构的物体模型及其高精度三维真实标注。通过使用几何结构已知的乐高模型作为三维结构进行图像采集，我们无需依赖高端三维扫描仪即可获得精确的三维真实标注。移动设备采集的高分辨率RGB图像与低分辨率深度图结合精确三维几何标注的独特数据模态，为未来高保真三维重建研究提供了独特机遇。此外，我们在所提数据集上评估了多种三维重建算法。项目主页：http://code.active.vision/MobileBrick/