  We propose ViewAL, a novel active learning strategy for semantic segmentation
that exploits viewpoint consistency in multi-view datasets. Our core idea is
that inconsistencies in model predictions across viewpoints provide a very
reliable measure of uncertainty and encourage the model to perform well
irrespective of the viewpoint under which objects are observed. To incorporate
this uncertainty measure, we introduce a new viewpoint entropy formulation,
which is the basis of our active learning strategy. In addition, we propose
uncertainty computations on a superpixel level, which exploits inherently
localized signal in the segmentation task, directly lowering the annotation
costs. This combination of viewpoint entropy and the use of superpixels allows
to efficiently select samples that are highly informative for improving the
network. We demonstrate that our proposed active learning strategy not only
yields the best-performing models for the same amount of required labeled data,
but also significantly reduces labeling effort. For instance, our method
achieves 95% of maximum achievable network performance using only 7%, 17%, and
24% labeled data on SceneNet-RGBD, ScanNet, and Matterport3D, respectively. On
these datasets, the best state-of-the-art method achieves the same performance
with 14%, 27% and 33% labeled data. Finally, we demonstrate that labeling using
superpixels yields the same quality of ground-truth compared to labeling whole
images, but requires 25% less time.
