Separating 3D point clouds into individual instances is an important task for 3D vision. It is challenging due to the unknown and varying number of instances in a scene. Existing deep learning based works focus on a two-step pipeline: first learn a feature embedding and then cluster the points. Such a two-step pipeline leads to disconnected intermediate objectives. In this paper, we propose an integrated reformulation of 3D instance segmentation as a per-point classification problem. We propose ICM-3D, a single-step method to segment 3D instances via instantiated categorization. The augmented category information is automatically constructed from 3D spatial positions. We conduct extensive experiments to verify the effectiveness of ICM-3D and show that it obtains inspiring performance across multiple frameworks, backbones and benchmarks.
@article{arxiv.2108.11771,
title = {ICM-3D: Instantiated Category Modeling for 3D Instance Segmentation},
author = {Ruihang Chu and Yukang Chen and Tao Kong and Lu Qi and Lei Li},
journal= {arXiv preprint arXiv:2108.11771},
year = {2021}
}