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In this paper, we propose to employ a Gaussian map representation to estimateprecise location and count of 3D surface features, addressing the limitationsof state-of-the-art methods based on density estimation which struggle inpresence of local disturbances. Gaussian maps indicate probable object locationand can be generated directly from keypoint annotations avoiding laborious andcostly per-pixel annotations. We apply this method to the 3D spheroidal classof objects which can be projected into 2D shape representation enablingefficient processing by a neural network GNet, an improved UNet architecture,which generates the likely locations of surface features and their precisecount. We demonstrate a practical use of this technique for counting strawberryachenes which is used as a fruit quality measure in phenotyping applications.The results of training the proposed system on several hundreds of 3D scans ofstrawberries from a publicly available dataset demonstrate the accuracy andprecision of the system which outperforms the state-of-the-art density-basedmethods for this application.

Gaussian map predictions for 3D surface feature localisation and counting