mlsanityData to 7 Oct 2026

MobileBrick: Building LEGO for 3D Reconstruction on Mobile Devices

arXiv 2303.01932 ↗First posted 3 Mar 2023cs.CV cs.LGcited by 15

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Abstract (arXiv)

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/

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 25 of its 41 references that are in the map. First posted on arXiv 3 Mar 2023.

Builds on

25 of 41 references.

References · 25

  1. 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,088
  2. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  3. 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,701
  4. 2017Matterport3D: Learning from RGB-D Data in Indoor Environmentscited by 1,354
  5. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,264
  6. 2021Volume Rendering of Neural Implicit Surfacescited by 990
  7. 2021Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstructioncited by 961
  8. 2021MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereocited by 735
  9. 2018Open3D: A Modern Library for 3D Data Processingcited by 717
  10. 2021Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstructioncited by 646
  11. 2020Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understandingcited by 593
  12. 2019BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networkscited by 487
13 more
  1. 2021ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Datacited by 442
  2. 2021Neural RGB-D Surface Reconstructioncited by 312
  3. 20203D-FUTURE: 3D Furniture shape with TextUREcited by 306
  4. 2018Pix3D: Dataset and Methods for Single-Image 3D Shape Modelingcited by 218
  5. 2020Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotationscited by 155
  6. 2020Visibility-aware Multi-view Stereo Networkcited by 97
  7. 2016A Large Dataset of Object Scanscited by 64
  8. 2019Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimationcited by 61
  9. 2016SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground Truthcited by 31
  10. 2022BNV-Fusion: Dense 3D Reconstruction using Bi-level Neural Volume Fusioncited by 29
  11. 2022Translating a Visual LEGO Manual to a Machine-Executable Plancited by 15
  12. 2022A Real World Dataset for Multi-view 3D Reconstructioncited by 9
  13. 2020Multi-Scale Progressive Fusion Learning for Depth Map Super-Resolutioncited by 7

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Citing papers per year: 2023: 4; 2024: 3; 2025: 3; 2026: 5.

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