DANNCE (3-Dimensional Aligned Neural Network for Computational Ethology) is a convolutional neural network (CNN) that calculates the 3D positions of user-defined anatomical landmarks on behaving animals from videos taken at multiple angles. The key innovation of DANNCE compared to existing approaches for 2D keypoint detection in animals (e.g. LEAP, DeepLabCut) is that the network is fully 3D, so that it can learn about 3D image features and how cameras and landmarks relate to one another in 3D space.
Timothy Dunn; Jesse Marshall; Diego Aldarondo; William Wang; Kyle Severson
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