  Recently, 3D scenes parsing with deep learning approaches has been a heating
topic. However, current methods with fully-supervised models require manually
annotated point-wise supervision which is extremely user-unfriendly and
time-consuming to obtain. As such, training 3D scene parsing models with sparse
supervision is an intriguing alternative. We term this task as data-efficient
3D scene parsing and propose an effective two-stage framework named VIBUS to
resolve it by exploiting the enormous unlabeled points. In the first stage, we
perform self-supervised representation learning on unlabeled points with the
proposed Viewpoint Bottleneck loss function. The loss function is derived from
an information bottleneck objective imposed on scenes under different
viewpoints, making the process of representation learning free of degradation
and sampling. In the second stage, pseudo labels are harvested from the sparse
labels based on uncertainty-spectrum modeling. By combining data-driven
uncertainty measures and 3D mesh spectrum measures (derived from normal
directions and geodesic distances), a robust local affinity metric is obtained.
Finite gamma/beta mixture models are used to decompose category-wise
distributions of these measures, leading to automatic selection of thresholds.
We evaluate VIBUS on the public benchmark ScanNet and achieve state-of-the-art
results on both validation set and online test server. Ablation studies show
that both Viewpoint Bottleneck and uncertainty-spectrum modeling bring
significant improvements. Codes and models are publicly available at
https://github.com/AIR-DISCOVER/VIBUS.
