  A series of 2D (and 3D) keypoint estimation tasks are built upon heatmap
coordinate representation, i.e. a probability map that allows for learnable and
spatially aware encoding and decoding of keypoint coordinates on grids, even
allowing for sub-pixel coordinate accuracy. In this report, we aim to reproduce
the findings of DARK that investigated the 2D heatmap representation by
highlighting the importance of the encoding of the ground truth heatmap and the
decoding of the predicted heatmap to keypoint coordinates. The authors claim
that a) a more principled distribution-aware coordinate decoding method
overcomes the limitations of the standard techniques widely used in the
literature, and b), that the reconstruction of heatmaps from ground-truth
coordinates by generating accurate and continuous heatmap distributions lead to
unbiased model training, contrary to the standard coordinate encoding process
that quantizes the keypoint coordinates on the resolution of the input image
grid.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning