  Recently, progress in acquisition equipment such as LiDAR sensors has enabled
sensing increasingly spacious outdoor 3D environments. Making sense of such 3D
acquisitions requires fine-grained scene understanding, such as constructing
instance-based 3D scene segmentations. Commonly, a neural network is trained
for this task; however, this requires access to a large, densely annotated
dataset, which is widely known to be challenging to obtain. To address this
issue, in this work we propose to predict instance segmentations for 3D scenes
in an unsupervised way, without relying on ground-truth annotations. To this
end, we construct a learning framework consisting of two components: (1) a
pseudo-annotation scheme for generating initial unsupervised pseudo-labels; and
(2) a self-training algorithm for instance segmentation to fit robust, accurate
instances from initial noisy proposals. To enable generating 3D instance mask
proposals, we construct a weighted proxy-graph by connecting 3D points with
edges integrating multi-modal image- and point-based self-supervised features,
and perform graph-cuts to isolate individual pseudo-instances. We then build on
a state-of-the-art point-based architecture and train a 3D instance
segmentation model, resulting in significant refinement of initial proposals.
To scale to arbitrary complexity 3D scenes, we design our algorithm to operate
on local 3D point chunks and construct a merging step to generate scene-level
instance segmentations. Experiments on the challenging SemanticKITTI benchmark
demonstrate the potential of our approach, where it attains 13.3% higher
Average Precision and 9.1% higher F1 score compared to the best-performing
baseline. The code will be made publicly available at
https://github.com/artonson/autoinst.
