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arXiv · 2108.11771

ICM-3D: Instantiated Category Modeling for 3D Instance Segmentation

Ruihang Chu·Yukang Chen·Tao Kong·Lu Qi·Lei Li
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Abstract

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.

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Ruihang Chu, Yukang Chen, Tao Kong, Lu Qi, Lei Li. 2021-08-26. ICM-3D: Instantiated Category Modeling for 3D Instance Segmentation. https://arxiv.org/abs/2108.11771

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