  Instance segmentation on 3D point clouds (3DIS) is a longstanding challenge
in computer vision, where state-of-the-art methods are mainly based on full
supervision. As annotating ground truth dense instance masks is tedious and
expensive, solving 3DIS with weak supervision has become more practical. In
this paper, we propose GaPro, a new instance segmentation for 3D point clouds
using axis-aligned 3D bounding box supervision. Our two-step approach involves
generating pseudo labels from box annotations and training a 3DIS network with
the resulting labels. Additionally, we employ the self-training strategy to
improve the performance of our method further. We devise an effective Gaussian
Process to generate pseudo instance masks from the bounding boxes and resolve
ambiguities when they overlap, resulting in pseudo instance masks with their
uncertainty values. Our experiments show that GaPro outperforms previous weakly
supervised 3D instance segmentation methods and has competitive performance
compared to state-of-the-art fully supervised ones. Furthermore, we demonstrate
the robustness of our approach, where we can adapt various state-of-the-art
fully supervised methods to the weak supervision task by using our pseudo
labels for training. The source code and trained models are available at
https://github.com/VinAIResearch/GaPro.
