{"slug": "costa-a-cluster-centric-paradigm-for-annotation-free-open-set-semantic-of-aerial", "title": "COSTA: A Cluster-Centric Paradigm for Annotation-Free Open-Set Semantic Segmentation of Aerial Point Clouds with Domain Shifts", "summary": "Researchers introduced COSTA, a cluster-centric paradigm for annotation-free open-set semantic segmentation of aerial point clouds under domain shifts, achieving up to 70.09% mIoU when adapting from the DALES source domain to three target benchmarks. COSTA reformulates segmentation as cluster-level propagation, using test-time adaptation and open-vocabulary vision-language models to handle mixed semantic shifts without additional training.", "body_md": "arXiv:2608.18479v1 Announce Type: new\nAbstract: Semantic segmentation of aerial point cloud is trapped in a generalization crisis under distinct domain shifts. While test-time adaptation offers a privacy-preserving and computationally efficient way to adapt pre-trained models to unlabeled target-domain data during inference, existing methods, bound to closed-set label assumptions and non-scalable point-wise segmentation pipelines, still struggle with semantic shifts. We ask: can we adapt any given pre-trained aerial point cloud segmentation model to a shifted target domain at the inference phase alone, without additional training, while segmenting target-specific categories beyond the source label space on demand? This paper introduces COSTA, which breaks this limitation by shifting from closed-set point-wise adaptation to cluster-centric open-set semantic propagation. Our core discovery is that, once effectively adapted at test time, the rich feature distribution of aerial point clouds can be distilled into a compact set of well-separated semantic centroids that are transferable across label spaces. COSTA leverages this to reformulate open-set semantic segmentation as a cluster-level propagating process: it first bridges the domain gap through proven test-time adaptation, then groups each batch of target-domain points into a small set of semantic clusters based on the similarity distribution in the adapted feature space, and finally propagates high-confidence pseudo labels obtained from an open-vocabulary vision-language model to all points through cluster-level voting. This cluster-centric paradigm enables test-time adaptation of aerial point clouds under significant domain gaps with mixed semantic shifts. With DALES as the source domain, COSTA enables on-demand segmentation across three aerial point cloud benchmarks with distinct domains and heterogeneous category spaces, achieving up to 70.09% mIoU under this new setting.", "url": "https://wpnews.pro/news/costa-a-cluster-centric-paradigm-for-annotation-free-open-set-semantic-of-aerial", "canonical_source": "https://arxiv.org/abs/2608.18479", "published_at": "2026-08-20 04:00:00+00:00", "updated_at": "2026-08-20 04:13:41.283357+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "computer-vision"], "entities": ["COSTA", "DALES"], "alternates": {"html": "https://wpnews.pro/news/costa-a-cluster-centric-paradigm-for-annotation-free-open-set-semantic-of-aerial", "markdown": "https://wpnews.pro/news/costa-a-cluster-centric-paradigm-for-annotation-free-open-set-semantic-of-aerial.md", "text": "https://wpnews.pro/news/costa-a-cluster-centric-paradigm-for-annotation-free-open-set-semantic-of-aerial.txt", "jsonld": "https://wpnews.pro/news/costa-a-cluster-centric-paradigm-for-annotation-free-open-set-semantic-of-aerial.jsonld"}}