Seeing Beyond: Extrapolative Domain Adaptive Panoramic Segmentation

• URL: http://arxiv.org/abs/2603.15475v1
• Date: Mon, 16 Mar 2026 16:09:18 GMT
• Title: Seeing Beyond: Extrapolative Domain Adaptive Panoramic Segmentation
• Abstract summary: Cross-domain panoramic semantic segmentation has attracted growing interest as it enables comprehensive 360 scene understanding for real-world applications.<n>We formulate an open-set domain adaptation setting, and propose Extrapolative Domain Adaptive Panoramic (EDA-PSeg) framework.<n>EDA-PSeg achieves state-of-the-art performance, robust generalization to diverse viewing geometries, and resilience under varying environmental conditions.
• Score: 23.40590151933085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-domain panoramic semantic segmentation has attracted growing interest as it enables comprehensive 360° scene understanding for real-world applications. However, it remains particularly challenging due to severe geometric Field of View (FoV) distortions and inconsistent open-set semantics across domains. In this work, we formulate an open-set domain adaptation setting, and propose Extrapolative Domain Adaptive Panoramic Segmentation (EDA-PSeg) framework that trains on local perspective views and tests on full 360° panoramic images, explicitly tackling both geometric FoV shifts across domains and semantic uncertainty arising from previously unseen classes. To this end, we propose the Euler-Margin Attention (EMA), which introduces an angular margin to enhance viewpoint-invariant semantic representation, while performing amplitude and phase modulation to improve generalization toward unseen classes. Additionally, we design the Graph Matching Adapter (GMA), which builds high-order graph relations to align shared semantics across FoV shifts while effectively separating novel categories through structural adaptation. Extensive experiments on four benchmark datasets under camera-shift, weather-condition, and open-set scenarios demonstrate that EDA-PSeg achieves state-of-the-art performance, robust generalization to diverse viewing geometries, and resilience under varying environmental conditions. The code is available at https://github.com/zyfone/EDA-PSeg.

Related papers

• SphereSOD: Geometry-Structure Coupled Learning for 360 Salient Object Detection [75.77856258186854]
  360 salient object detection aims to accurately segment salient regions across a full field of view.<n>Existing methods mainly focus on compensating projection distortion while overlooking the interaction between panoramic geometry and salient object structure.<n>We propose SphereSOD, an ERP-native framework that couples panoramic geometry with evolving salient structures.
  arXiv  Detail & Related papers  (2026-09-07T14:47:54Z)
• Hierarchical Prompt Injector for Domain Generalization Segmentation [21.255611918751104]
  Domain Generalized Semantic (DGSS) is a challenging task, as vision models often rely on low-level appearance cues that change across domains.<n>Existing methods use prompt learning to transfer structural priors into DGSS models, but typically encode each class as a single holistic prompt.<n>We address this with textbfSpatial Hierarchical Prompts (SHP) that enrich each class with region-level geometric anchors capturing structural appearance from distinct viewing angles.
  arXiv  Detail & Related papers  (2026-09-05T03:56:00Z)
• BGG: Bridging the Geometric Gap between Cross-View images by Vision Foundation Model Adaptation for Geo-Localization [50.74663742490919]
  Cross-View Geo-Localization (CVGL) aims to acquire the geolocation of images by image retrieval.<n>This paper proposes a parameter-efficient framework for bridging the geometric gap across images based on the vision foundation model (VFM)<n>It mainly contains a Multi-granularity Feature Enhancement Adapter (MFEA) and a Frequency-Aware Structural Aggregation (FASA) module.
  arXiv  Detail & Related papers  (2026-05-11T10:46:33Z)
• SAGE: Style-Adaptive Generalization for Privacy-Constrained Semantic Segmentation Across Domains [13.393232074517387]
  textbfSAGE improves the generalization of frozen models under privacy constraints.<n>We first utilize style transfer to construct a diverse style representation of the source domain.<n>Then, the model adaptively fuses these style cues according to the visual context of each input, forming a dynamic prompt.
  arXiv  Detail & Related papers  (2025-12-02T03:20:22Z)
• Leveraging Depth and Language for Open-Vocabulary Domain-Generalized Semantic Segmentation [8.068623902839368]
  Open-Vocabulary semantic segmentation (OVSS) and domain generalization in semantic segmentation (DGSS) highlight a subtle complementarity.<n>OV-DGSS aims to generate pixel-level masks for unseen categories while maintaining robustness across unseen domains.<n>We introduce Vireo, a novel single-stage framework for OV-DGSS that unifies the strengths of OVSS and DGSS for the first time.
  arXiv  Detail & Related papers  (2025-06-11T15:54:47Z)
• EDAPS: Enhanced Domain-Adaptive Panoptic Segmentation [93.25977558780896]
  We study the panoptic network design and propose a novel architecture (EDAPS) designed explicitly for domain-adaptive panoptic segmentation. EDAPS significantly improves the state-of-the-art performance for panoptic segmentation UDA by a large margin of 20% on SYNTHIA-to-Cityscapes and even 72% on the more challenging SYNTHIA-to-Mapillary Vistas.
  arXiv  Detail & Related papers  (2023-04-27T15:51:19Z)
• Behind Every Domain There is a Shift: Adapting Distortion-aware Vision Transformers for Panoramic Semantic Segmentation [73.48323921632506]
  We address panoramic semantic segmentation which is under-explored due to two critical challenges. First, we propose an upgraded Transformer for Panoramic Semantic, i.e., Trans4PASS+, equipped with Deformable Patch Embedding (DPE) and Deformable (DMLPv2) modules. Second, we enhance the Mutual Prototypical Adaptation (MPA) strategy via pseudo-label rectification for unsupervised domain adaptive panoramic segmentation. Third, aside from Pinhole-to-Panoramic (Pin2Pan) adaptation, we create a new dataset (SynPASS) with 9,080 panoramic images
  arXiv  Detail & Related papers  (2022-07-25T00:42:38Z)
• Panoramic Panoptic Segmentation: Insights Into Surrounding Parsing for Mobile Agents via Unsupervised Contrastive Learning [93.6645991946674]
  We introduce panoramic panoptic segmentation, as the most holistic scene understanding. A complete surrounding understanding provides a maximum of information to a mobile agent. We propose a framework which allows model training on standard pinhole images and transfers the learned features to a different domain.
  arXiv  Detail & Related papers  (2022-06-21T20:07:15Z)
• Transfer beyond the Field of View: Dense Panoramic Semantic Segmentation via Unsupervised Domain Adaptation [30.104947024614127]
  We formalize the task of unsupervised domain adaptation for panoramic semantic segmentation. DensePASS is a novel dataset for panoramic segmentation under cross-domain conditions. We introduce P2PDA - a generic framework for Pinhole-to-Panoramic semantic segmentation.
  arXiv  Detail & Related papers  (2021-10-21T11:22:05Z)
• PIT: Position-Invariant Transform for Cross-FoV Domain Adaptation [53.428312630479816]
  We observe that the Field of View (FoV) gap induces noticeable instance appearance differences between the source and target domains. Motivated by the observations, we propose the textbfPosition-Invariant Transform (PIT) to better align images in different domains.
  arXiv  Detail & Related papers  (2021-08-16T15:16:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.