Improved Block Merging for 3D Point Cloud Instance Segmentation

Fuente: arXiv
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Auteurs principaux: Denis, Leon, Royen, Remco, Munteanu, Adrian
Format: Preprint
Publié: 2024
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author Denis, Leon
Royen, Remco
Munteanu, Adrian
author_facet Denis, Leon
Royen, Remco
Munteanu, Adrian
contents This paper proposes a novel block merging algorithm suitable for any block-based 3D instance segmentation technique. The proposed work improves over the state-of-the-art by allowing wrongly labelled points of already processed blocks to be corrected through label propagation. By doing so, instance overlap between blocks is not anymore necessary to produce the desirable results, which is the main limitation of the current art. Our experiments show that the proposed block merging algorithm significantly and consistently improves the obtained accuracy for all evaluation metrics employed in literature, regardless of the underlying network architecture.
format Preprint
id arxiv_https___arxiv_org_abs_2407_06991
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Improved Block Merging for 3D Point Cloud Instance Segmentation
Denis, Leon
Royen, Remco
Munteanu, Adrian
Computer Vision and Pattern Recognition
This paper proposes a novel block merging algorithm suitable for any block-based 3D instance segmentation technique. The proposed work improves over the state-of-the-art by allowing wrongly labelled points of already processed blocks to be corrected through label propagation. By doing so, instance overlap between blocks is not anymore necessary to produce the desirable results, which is the main limitation of the current art. Our experiments show that the proposed block merging algorithm significantly and consistently improves the obtained accuracy for all evaluation metrics employed in literature, regardless of the underlying network architecture.
title Improved Block Merging for 3D Point Cloud Instance Segmentation
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2407.06991