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Research Article | Open Access

Semantic segmentation-assisted instance feature fusion for multi-level 3D part instance segmentation

Institute for Advanced Study, Tsinghua University, Beijing 100084, China
Microsoft Research Asia, Beijing 100080, China
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Graphical Abstract

Abstract

Recognizing 3D part instances from a 3D point cloud is crucial for 3D structure and scene understanding. Several learning-based approaches use semantic segmentation and instance center prediction as training tasks and fail to further exploit the inherent relationship between shape semantics and part instances. In this paper, we present a new method for 3D part instance segmentation. Our method exploits semantic segmentation to fuse nonlocal instance fea-tures, such as center prediction, and further enhances the fusion scheme in a multi- and cross-level way. We also propose a semantic region center prediction task to train and leverage the prediction results to improve the clustering of instance points. Our method outperforms existing methods with a large-margin improvement in the PartNet benchmark. We also demonstrate that our feature fusion scheme can be applied to other existing methods to improve their performance in indoor scene instance segmentation tasks.

Keywords

3D part instance segmentationfeature fusion3D deep learning

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Computational Visual Media
Volume 9 Issue 4,
December 2023
Pages 699-715
DOI: 10.1007/s41095-022-0300-x
Cite this article:
Sun C-Y, Tong X, Liu Y. Semantic segmentation-assisted instance feature fusion for multi-level 3D part instance segmentation. Computational Visual Media, 2023, 9(4): 699-715. https://doi.org/10.1007/s41095-022-0300-x

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Received: 08 April 2022
Accepted: 03 June 2022
Published: 30 June 2023
© The Author(s) 2023.

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