Weakly Supervised 3D Instance Segmentation without Instance-level
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Shichao Dong Guosheng Lin
3D semantic scene understanding tasks have achieved great success with the
emergence of deep learning, but often require a huge amount of manually
annotated training data. To alleviate the annotation cost, we propose the first
weakly-supervised 3D instance segmentation method that only requires
categorical semantic labels as supervision, and we do not need instance-level
labels. The required semantic annotations can be either dense or extreme sparse
(e.g. 0.02% of total points). Even without having any instance-related
ground-truth, we design an approach to break point clouds into raw fragments
and find the most confident samples for learning instance centroids.
Furthermore, we construct a recomposed dataset using pseudo instances, which is
used to learn our defined multilevel shape-aware objectness signal. An
asymmetrical object inference algorithm is followed to process core points and
boundary points with different strategies, and generate high-quality pseudo
instance labels to guide iterative training. Experiments demonstrate that our
method can achieve comparable results with recent fully supervised methods. By
generating pseudo instance labels from categorical semantic labels, our
designed approach can also assist existing methods for learning 3D instance
segmentation at reduced annotation cost.