  Semantic understanding of 3D point cloud relies on learning models with
massively annotated data, which, in many cases, are expensive or difficult to
collect. This has led to an emerging research interest in semi-supervised
learning (SSL) for 3D point cloud. It is commonly assumed in SSL that the
unlabeled data are drawn from the same distribution as that of the labeled
ones; This assumption, however, rarely holds true in realistic environments.
Blindly using out-of-distribution (OOD) unlabeled data could harm SSL
performance. In this work, we propose to selectively utilize unlabeled data
through sample weighting, so that only conducive unlabeled data would be
prioritized. To estimate the weights, we adopt a bi-level optimization
framework which iteratively optimizes a metaobjective on a held-out validation
set and a task-objective on a training set. Faced with the instability of
efficient bi-level optimizers, we further propose three regularization
techniques to enhance the training stability. Extensive experiments on 3D point
cloud classification and segmentation tasks verify the effectiveness of our
proposed method. We also demonstrate the feasibility of a more efficient
training strategy.
