  This paper presents a novel method for instance segmentation of 3D point
clouds. The proposed method is called Gaussian Instance Center Network (GICN),
which can approximate the distributions of instance centers scattered in the
whole scene as Gaussian center heatmaps. Based on the predicted heatmaps, a
small number of center candidates can be easily selected for the subsequent
predictions with efficiency, including i) predicting the instance size of each
center to decide a range for extracting features, ii) generating bounding boxes
for centers, and iii) producing the final instance masks. GICN is a
single-stage, anchor-free, and end-to-end architecture that is easy to train
and efficient to perform inference. Benefited from the center-dictated
mechanism with adaptive instance size selection, our method achieves
state-of-the-art performance in the task of 3D instance segmentation on ScanNet
and S3DIS datasets.
