  For image recognition and labeling tasks, recent results suggest that machine
learning methods that rely on manually specified feature representations may be
outperformed by methods that automatically derive feature representations based
on the data. Yet for problems that involve analysis of 3d objects, such as mesh
segmentation, shape retrieval, or neuron fragment agglomeration, there remains
a strong reliance on hand-designed feature descriptors. In this paper, we
evaluate a large set of hand-designed 3d feature descriptors alongside features
learned from the raw data using both end-to-end and unsupervised learning
techniques, in the context of agglomeration of 3d neuron fragments. By
combining unsupervised learning techniques with a novel dynamic pooling scheme,
we show how pure learning-based methods are for the first time competitive with
hand-designed 3d shape descriptors. We investigate data augmentation strategies
for dramatically increasing the size of the training set, and show how
combining both learned and hand-designed features leads to the highest
accuracy.
