Building LEGO for 3D Reconstruction on Mobile Devices

Kejie Li · Jia-Wang Bian · Robert Castle · Philip H.S. Torr · Victor Adrian Prisacariu

Project Page | arXiv | Dataset

Even 3D scanners can only generate pseudo ground-truth shapes with artefacts. MobileBrick is the first multi-view RGBD dataset, captured on a mobile device, with precise 3D annotations for detailed 3D object reconstruction.

We propose a novel data capturing and 3D annotation pipeline in MobileBrick without relying on expensive 3D scanners. The key to creating the precise 3D ground-truth shapes is using LEGO models, which are made of LEGO bricks with known geometry. The data modality of RGBD images captured on a mobile device paired with exact 3D geometry annotations provides a unique opportunity for future research on high-fidelity 3D reconstruction.

Overview

Install
Our dataset
Evaluation
Cite
Changelog

Install

you can install dependencies with Anaconda as follows:

conda env create -f environment.yml
conda activate mobilebrick

Dataset Organisation

The dataset is organised by sequences, with 135 sequences of random shapes can be used for training, and 18 sequences of manually curated LEGO models for evaluation.

A sequence contains the following structure:


SEQUENCE_NAME
├── arkit_depth (the confidence and depth maps provided by ARKit)
|    ├── 000000_conf.png
|    ├── 000000.png
|    ├── ...
├── gt_depth (The high-resolution depth maps projected from the aligned GT shape)
|    ├── 000000.png
|    ├── ...     
├── image (the RGB images)
|    ├── 000000.jpg
|    ├── ...
├── mask (object foreground mask projected from the aligned GT shape)
|    ├── 000000.png
|    ├── ...
├── intrinsic (3x3 intrinsic matrix of each image)
|    ├── 000000.txt
|    ├── ...
├── pose (4x4 transformation matrix from camera to world of each image)
|    ├── 000000.txt
|    ├── ...
├── mesh
|    ├── gt_mesh.ply
├── visibility_mask.npy (the visibility mask to be used for evaluation)
├── cameras.npz (processed camera poses using the format of NeuS)

Note:

the gt_mesh.ply is created by running tsdf-fusion using the gt depth

Evaluation

We provide scripts to run evaluation on 3D reconstruction and Novel View Synthesis (NVS).

To evaluate 3D reconstruction, use the following code.

python evaluations/evaluate_3d.py --method $METHOD

The reconstruction files (.ply) to be evaluated should be places in the ./meshes/$METHOD folder. A .csv file with per-sequence results will be generated.

To evaluate NVS, use the following code.

python evaluate_nvs.py --method $METHOD

The rendered images for evaluation should be placed in ./nvs/$METHOD

Cite

Please cite our work if you find it useful or use any of our code

@article{li2023mobilebrick,
  author = {Kejie Li, Jia-Wang Bian, Robert Castle, Philip H.S. Torr, Victor Adrian Prisacariu},
  title = {MobileBrick: Building LEGO for 3D Reconstruction on Mobile Devices},
  journal={arXiv preprint arXiv:2303.01932},
  year={2023}
}

Changelog

09/03/2023: MobileBrick is merged into Voxurf, see instructions on their repo.
06/03/2023: Dataset is online

ActiveVisionLab/MobileBrick