  Semantic understanding of 3D point clouds is important for various robotics
applications. Given that point-wise semantic annotation is expensive, in this
paper, we address the challenge of learning models with extremely sparse
labels. The core problem is how to leverage numerous unlabeled points. To this
end, we propose a self-supervised 3D representation learning framework named
viewpoint bottleneck. It optimizes a mutual-information based objective, which
is applied on point clouds under different viewpoints. A principled analysis
shows that viewpoint bottleneck leads to an elegant surrogate loss function
that is suitable for large-scale point cloud data. Compared with former arts
based upon contrastive learning, viewpoint bottleneck operates on the feature
dimension instead of the sample dimension. This paradigm shift has several
advantages: It is easy to implement and tune, does not need negative samples
and performs better on our goal down-streaming task. We evaluate our method on
the public benchmark ScanNet, under the pointly-supervised setting. We achieve
the best quantitative results among comparable solutions. Meanwhile we provide
an extensive qualitative inspection on various challenging scenes. They
demonstrate that our models can produce fairly good scene parsing results for
robotics applications. Our code, data and models will be made public.
