Learning Gaussian Instance Segmentation in Point Clouds

Understand

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.