  Humans are able to segment images effortlessly without supervision using
perceptual grouping. In this work, we propose a counter-intuitive computational
approach to solving unsupervised perceptual grouping and segmentation: that
they arise \textit{because} of neural noise, rather than in spite of it. We (1)
mathematically demonstrate that under realistic assumptions, neural noise can
be used to separate objects from each other; (2) that adding noise in a DNN
enables the network to segment images even though it was never trained on any
segmentation labels; and (3) that segmenting objects using noise results in
segmentation performance that aligns with the perceptual grouping phenomena
observed in humans, and is sample-efficient. We introduce the Good Gestalt (GG)
datasets -- six datasets designed to specifically test perceptual grouping, and
show that our DNN models reproduce many important phenomena in human
perception, such as illusory contours, closure, continuity, proximity, and
occlusion. Finally, we (4) show that our model improves performance on our GG
datasets compared to other tested unsupervised models by $24.9\%$. Together,
our results suggest a novel unsupervised segmentation method requiring few
assumptions, a new explanation for the formation of perceptual grouping, and a
novel potential benefit of neural noise.
