Aug 9, 2022 · In this paper, we propose a graph-based framework to explore the local-level feature alignment between the two domains, which can reserve semantic ...
Missing: Adaptive | Show results with:Adaptive
Aug 9, 2022 · However, most of these methods focus on fully supervised learning for point cloud segmentation with a large number of manually annotated labels.
May 11, 2022 · We propose a novel domain consistency framework for unsupervised domain adaptive point cloud semantic segmentation.
This paper tackles the domain adaptation problem in point cloud semantic segmentation, which performs adap- tation from a fully labeled domain (source ...
Specifically, in our framework, we construct a multi-level feature consistency model to generate the high quality pseudo labels for the unlabeled target domain.
Unsupervised Domain Adaptation for Point Cloud ... - Semantic Scholar
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Unsupervised domain adaptation for point cloud semantic segmentation has attracted great attention due to its effectiveness in learning with unlabeled data ...
Achituve, I., Maron, H., Chechik, G.: Self-supervised learning for domain adaptation on point clouds. In: WACV (2021)
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What is the unsupervised domain adaptation approach?
What is point cloud semantic segmentation?
We propose a new approach of sample mixing for point cloud UDA, namely Compositional Semantic Mix (CoSMix), the first UDA approach for point cloud segmentation.
Missing: Adaptive | Show results with:Adaptive
May 16, 2023 · An Unsupervised Domain Adaptive for Semantic Segmentation (UDASS) method based on DAFormer improved model is proposed.
Oct 6, 2023 · This process involves categorizing individual points within LiDAR scans into specific semantic classes, thereby providing crucial semantic ...