  Existing 3D instance segmentation methods are predominated by the bottom-up
design -- manually fine-tuned algorithm to group points into clusters followed
by a refinement network. However, by relying on the quality of the clusters,
these methods generate susceptible results when (1) nearby objects with the
same semantic class are packed together, or (2) large objects with loosely
connected regions. To address these limitations, we introduce ISBNet, a novel
cluster-free method that represents instances as kernels and decodes instance
masks via dynamic convolution. To efficiently generate high-recall and
discriminative kernels, we propose a simple strategy named Instance-aware
Farthest Point Sampling to sample candidates and leverage the local aggregation
layer inspired by PointNet++ to encode candidate features. Moreover, we show
that predicting and leveraging the 3D axis-aligned bounding boxes in the
dynamic convolution further boosts performance. Our method set new
state-of-the-art results on ScanNetV2 (55.9), S3DIS (60.8), and STPLS3D (49.2)
in terms of AP and retains fast inference time (237ms per scene on ScanNetV2).
The source code and trained models are available at
https://github.com/VinAIResearch/ISBNet.
