Spherical Mask: Coarse-to-Fine 3D Point Cloud Instance Segmentation with
  Spherical Representation
Sangyun Shin Kaichen Zhou Madhu Vankadari Andrew Markham Niki Trigoni
Coarse-to-fine 3D instance segmentation methods show weak performances
compared to recent Grouping-based, Kernel-based and Transformer-based methods.
We argue that this is due to two limitations: 1) Instance size overestimation
by axis-aligned bounding box(AABB) 2) False negative error accumulation from
inaccurate box to the refinement phase. In this work, we introduce Spherical
Mask, a novel coarse-to-fine approach based on spherical representation,
overcoming those two limitations with several benefits. Specifically, our
coarse detection estimates each instance with a 3D polygon using a center and
radial distance predictions, which avoids excessive size estimation of AABB. To
cut the error propagation in the existing coarse-to-fine approaches, we
virtually migrate points based on the polygon, allowing all foreground points,
including false negatives, to be refined. During inference, the proposal and
point migration modules run in parallel and are assembled to form binary masks
of instances. We also introduce two margin-based losses for the point migration
to enforce corrections for the false positives/negatives and cohesion of
foreground points, significantly improving the performance. Experimental
results from three datasets, such as ScanNetV2, S3DIS, and STPLS3D, show that
our proposed method outperforms existing works, demonstrating the effectiveness
of the new instance representation with spherical coordinates.