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Spherical Geometry-Aware Panoptic Scene Graph Generation for Equirectangular Panoramas

2026-10-20 11:23:03
Zhe Wu, Xiaoyan Fei, Xinyi Jiang, Yanxu Wei, Junming Rao, Yihong Yan, Xuewei Li
arXiv_CV
         
Abstract

Panoptic scene graph generation (PSGG) represents scene entities with segmentation masks and directed relations. Equirectangular projection (ERP) panoramas challenge PSGG through distortion, latitude-dependent pixel areas, and an artificial seam. We propose a two-stage spherical geometry-aware framework that fuses overlapping perspective-view predictions using observation reliability, spherical overlap, and angular proximity. Relation reasoning combines spherical-area-aware aggregation, weighted no-relation supervision, and dual-longitude score fusion. We also construct a dataset of 340 high-resolution panoramas with 10,571 nodes and 6,632 directed relations. Against corresponding baselines, spherical fusion improves PQ by 36.03% under matched views, while relation reasoning improves mR@50 by 11.20% under PredCls with ground-truth masks and object labels. The full framework improves mR@50 by 52.99% under SGDet with predicted panoptic segments. These results demonstrate the value of spherical geometry for panoramic PSGG.

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URL

https://arxiv.org/abs/2610.28033

PDF

https://arxiv.org/pdf/2610.28033.pdf

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