# Gaussian map predictions for 3D surface feature localisation and counting

arXiv: https://arxiv.org/abs/2112.03736 · HTML: https://mlsanity.com/paper/2112.03736/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 31 references that are in the map.
- First posted on arXiv 7 Dec 2021.

## Builds on (most cited first, 17 of 17)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Spherical CNNs](https://mlsanity.com/paper/1801.10130/) (2018)
- [CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes](https://mlsanity.com/paper/1802.10062/) (2018)
- [PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection](https://mlsanity.com/paper/2102.00463/) (2021)
- [Context-Aware Crowd Counting](https://mlsanity.com/paper/1811.10452/) (2018)
- [DeepSphere: a graph-based spherical CNN](https://mlsanity.com/paper/2012.15000/) (2020)
- [DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applications](https://mlsanity.com/paper/1810.12186/) (2018)
- [Surface Networks via General Covers](https://mlsanity.com/paper/1812.10705/) (2018)
- [DeepSphere: towards an equivariant graph-based spherical CNN](https://mlsanity.com/paper/1904.05146/) (2019)
- [3D Object Classification via Spherical Projections](https://mlsanity.com/paper/1712.04426/) (2017)
- [Joint Stem Detection and Crop-Weed Classification for Plant-specific Treatment in Precision Farming](https://mlsanity.com/paper/1806.03413/) (2018)
- [SPNet: Deep 3D Object Classification and Retrieval using Stereographic Projection](https://mlsanity.com/paper/1811.01571/) (2018)
- [Detection of Single Grapevine Berries in Images Using Fully Convolutional Neural Networks](https://mlsanity.com/paper/1905.00458/) (2019)
- [AutoCount: Unsupervised Segmentation and Counting of Organs in Field Images](https://mlsanity.com/paper/2007.09178/) (2020)
- [Learning Gaussian Maps for Dense Object Detection](https://mlsanity.com/paper/2004.11855/) (2020)

## Abstract (arXiv)

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.