  Manually annotating complex scene point cloud datasets is both costly and
error-prone. To reduce the reliance on labeled data, a new model called
SnapshotNet is proposed as a self-supervised feature learning approach, which
directly works on the unlabeled point cloud data of a complex 3D scene. The
SnapshotNet pipeline includes three stages. In the snapshot capturing stage,
snapshots, which are defined as local collections of points, are sampled from
the point cloud scene. A snapshot could be a view of a local 3D scan directly
captured from the real scene, or a virtual view of such from a large 3D point
cloud dataset. Snapshots could also be sampled at different sampling rates or
fields of view (FOVs), thus multi-FOV snapshots, to capture scale information
from the scene. In the feature learning stage, a new pre-text task called
multi-FOV contrasting is proposed to recognize whether two snapshots are from
the same object or not, within the same FOV or across different FOVs. Snapshots
go through two self-supervised learning steps: the contrastive learning step
with both part and scale contrasting, followed by a snapshot clustering step to
extract higher level semantic features. Then a weakly-supervised segmentation
stage is implemented by first training a standard SVM classifier on the learned
features with a small fraction of labeled snapshots. The trained SVM is used to
predict labels for input snapshots and predicted labels are converted into
point-wise label assignments for semantic segmentation of the entire scene
using a voting procedure. The experiments are conducted on the Semantic3D
dataset and the results have shown that the proposed method is capable of
learning effective features from snapshots of complex scene data without any
labels. Moreover, the proposed method has shown advantages when comparing to
the SOA method on weakly-supervised point cloud semantic segmentation.
