Gaussian map predictions for 3D surface feature localisation and counting. Louedec, J. L. & Cielniak, G. In BMVC, November, 2021. BMVA.
Gaussian map predictions for 3D surface feature localisation and counting [link]Paper  abstract   bibtex   
In this paper, we propose to employ a Gaussian map representation to estimate precise location and count of 3D surface features, addressing the limitations of state-of-the-art methods based on density estimation which struggle in presence of local disturbances. Gaussian maps indicate probable object location and can be generated directly from keypoint annotations avoiding laborious and costly per-pixel annotations. We apply this method to the 3D spheroidal class of objects which can be projected into 2D shape representation enabling efficient processing by a neural network GNet, an improved UNet architecture, which generates the likely locations of surface features and their precise count. We demonstrate a practical use of this technique for counting strawberry achenes which is used as a fruit quality measure in phenotyping applications. The results of training the proposed system on several hundreds of 3D scans of strawberries from a publicly available dataset demonstrate the accuracy and precision of the system which outperforms the state-of-the-art density-based methods for this application.
@inproceedings{lincoln48667,
       booktitle = {BMVC},
           month = {November},
           title = {Gaussian map predictions for 3D surface feature localisation and counting},
          author = {Justin Le Louedec and Grzegorz Cielniak},
       publisher = {BMVA},
            year = {2021},
        keywords = {ARRAY(0x56546f012538)},
             url = {https://eprints.lincoln.ac.uk/id/eprint/48667/},
        abstract = {In this paper, we propose to employ a Gaussian map representation to estimate precise location and count of 3D surface features, addressing the limitations of state-of-the-art methods based on density estimation which struggle in presence of local disturbances. Gaussian maps indicate probable object location and can be generated directly from keypoint annotations avoiding laborious and costly per-pixel annotations. We apply this method to the 3D spheroidal class of objects which can be projected into 2D shape representation enabling efficient processing by a neural network GNet, an improved UNet architecture, which generates the likely locations of surface features and their precise count. We demonstrate a practical use of this technique for counting strawberry achenes which is used as a fruit quality measure in phenotyping applications. The results of training the proposed system on several hundreds of 3D scans of strawberries from a publicly available dataset demonstrate the accuracy and precision of the system which outperforms the state-of-the-art density-based methods for this application.}
}
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