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Multi-view 3D Objects Localization from Street Level Scenes

Paper (link.springer.com/chapter/10.1007/978-3-031-06430-2_8)

@inproceedings{ahmad2022multi,
  title={Multi-view 3D Objects Localization from Street-Level Scenes},
  author={Ahmad, Javed and Toso, Matteo and Taiana, Matteo and James, Stuart and Del Bue, Alessio},
  booktitle={International Conference on Image Analysis and Processing},
  pages={89--101},
  year={2022},
  organization={Springer}
}

Contact

Any questions or suggestions are welcomed!

Javed Ahmad javed.ahmad@iit.it , javed.sial91@gmail.com

Abstract:

This paper presents a method to localize street-level objects in 3D from images of an urban area. Our method processes 3D sparse point clouds reconstructed from multi-view images and leverages 2D instance segmentation to find all objects within the scene and to generate for each object the corresponding cluster of 3D points and matched 2D detections. The proposed approach is robust to changes in image sizes, viewpoint changes, and changes in the object’s appearance across different views. We validate our approach on challenging street-level crowd-sourced images from the Mapillary platform, showing a significant improvement in the mean average precision of object localization for the available Mapillary annotations. These results showcase our method’s effectiveness in localizing objects in 3D, which could potentially be used in applications such as high-definition map generation of urban environments.

The organization of code is same as the method explained in the block diagram of paper.

└── All Steps
       ├── Download images and instance segmentation from Mapillary
       ├── Run SfM sparse reconstruction
       ├── Set paths and parameters in the config/setting.yaml
       ├── Run script for step 1-2 that localizes objects in the scene
       ├── Run scripe for step 3 that matches the 2D detections

Mapillary street-level scenes

You can download the images captured at a particular area/scene of any city where Mapillary service is available by following this blog post https://blog.mapillary.com/update/2021/12/03/mapillary-python-sdk.html.

Configuration

Set the path of the downloaded scene and the other hyper-parameters in the following file

config/setting.yaml

Process data and localize/find 3D objects available in the scene

Use the following script, it loads instance segmentation, images, and sparse reconstruction to refine the sparse point clouds and cluster them to find the objects in the scene.

python Step_1_and_2_Refine_Scene_and_Clustering.py

Provide matched 2D detection

Use the following script, it loads localized 3D objects in the scene and matches 2D detections based on that, and writes all matched detection of an instance in one folder. The settings can be changed to have all matched detection together.

python Step_2_MatchingDetection.py

Evaluation

The code for evaluation on two scenes as mentioned in the paper will be available soon.