/MosaicMVS

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MosaicMVS: Mosaic-based Omnidirectional Multi-view Stereo for Indoor Scenes

Figure_1_2

This is the official implementation for the 2023 Transactions on Multimedia (Early Access) paper, MosaicMVS: Mosaic-based Omnidirectional Multi-view Stereo for Indoor Scenes.

As we cannot share the entire dataset, only provide some pictures of scene1 for testing.

For testing our code, run the shell file:

Installation requirements

We run this code using GeForce RTX 3090 in following environment.

python >=3.7
Pytorch ==1.10
CUDA >=9.0

Data preparation

SAOI Dataset

This dataset consists of 11 scenes.

Parts of the scene 1 RGB images in SAOI dataset are uploaded in scene1 folder for implementation.

For more datasets you needed, please contact us.

Customed mosaic images

If you capture customed mosaic images, you must run the COLMAP to obtain undistorted image, and camera parameters. You need following datas with your images,

Undistorted images (RGB)
Intrinsic 
Extrinsics
View selection text (pair.txt)

Testing

To run this code,

git clone https://github.com/min-jung-shin/MosaicMVS.git
sh test_mosaic.sh

Fusion

To fusion output depthmaps,

CUDA_VISIBLE_DEVICES=0,1 python fusioncas.py -data <dir_of_depths> --pair <dir_of_pair> --vthresh 3 --pthresh .8,.8,.8 --outmask <dir_of_masks> --out_dir <dir_of_output pointcloud>

For reconstrction evaluation, refer to the code in the python fusioncas.py.

("total.mean: ", sum(total)/len(total))

Evaluation of estimated depth map

To evaluate depth map, COLMAP sparse reconstruction depthmaps are needed. You can run the COLMAP.

Acknowledgements

Thanks to Xiaodong Gu for opening source of his excellent work CasMVSNet. Thanks to Jingyang Zhang of his excellent work Vis-MVSNet.