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

• URL: http://arxiv.org/abs/2108.11771v1
• Date: Thu, 26 Aug 2021 13:08:37 GMT
• Title: ICM-3D: Instantiated Category Modeling for 3D Instance Segmentation
• Authors: Ruihang Chu, Yukang Chen, Tao Kong, Lu Qi and Lei Li
• Abstract summary: We propose ICM-3D, a single-step method to segment 3D instances via instantiated categorization. We conduct extensive experiments to verify the effectiveness of ICM-3D and show that it obtains inspiring performance across multiple frameworks, backbones and benchmarks.
• Score: 19.575077449759377
• License: http://creativecommons.org/licenses/by/4.0/
• 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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