Existing state-of-the-art 3D point cloud instance segmentation methods rely
on a grouping-based approach that groups points to obtain object instances.
Despite improvement in producing accurate segmentation results, these methods
lack scalability and commonly require dividing large input into multiple parts.
To process a scene with millions of points, the existing fastest method
SoftGroup \cite{vu2022softgroup} requires tens of seconds, which is under
satisfaction. Our finding is that k-Nearest Neighbor (k-NN), which serves
as the prerequisite of grouping, is a computational bottleneck. This bottleneck
severely worsens the inference time in the scene with a large number of points.
This paper proposes SoftGroup++ to address this computational bottleneck and
further optimize the inference speed of the whole network. SoftGroup++ is built
upon SoftGroup, which differs in three important aspects: (1) performs octree
k-NN instead of vanilla k-NN to reduce time complexity from
O(n2) to O(nlogn), (2) performs pyramid scaling
that adaptively downsamples backbone outputs to reduce search space for k-NN
and grouping, and (3) performs late devoxelization that delays the conversion
from voxels to points towards the end of the model such that intermediate
components operate at a low computational cost. Extensive experiments on
various indoor and outdoor datasets demonstrate the efficacy of the proposed
SoftGroup++. Notably, SoftGroup++ processes large scenes of millions of points
by a single forward without dividing the input into multiple parts, thus
enriching contextual information. Especially, SoftGroup++ achieves 2.4 points
AP50 improvement while nearly 6× faster than the existing fastest
method on S3DIS dataset. The code and trained models will be made publicly
available.Comment: Technical repor