SphereSOD: Geometry-Structure Coupled Learning for 360 Salient Object Detection

2026-09-07

AI Summary

针对360°全景图像显著物体检测中的空间失真问题，提出SphereSOD方法，通过结合全景几何与显著结构进行特征采样和上下文聚合，实现准确的显著区域分割。

Abstract

360{\deg} salient object detection (SOD) aims to accurately segment salient regions across a full field of view. However, equirectangular projection (ERP) introduces severe spatial distortion when mapping the spherical domain onto a planar representation. Existing methods mainly focus on compensating projection distortion while overlooking the interaction between panoramic geometry and salient object structure during feature perception and prediction refinement. To this end, we propose SphereSOD, an ERP-native framework that couples panoramic geometry with evolving salient structures. Specifically, spherical geometry governs feature sampling and spatial weighting, while coarse-grained saliency and contour prediction influence context aggregation during the progressive decoding process. SphereSOD first initializes deformable sampling based on spherical projection geometry and then employs bounded, content-adaptive offsets, yielding features that are better aligned with the underlying panoramic geometry. Subsequently, the decoder performs structure-guided context aggregation and progressive refinement to recover complete salient regions and accurate boundaries. Extensive experiments on three public 360{\deg} SOD benchmarks demonstrate state-of-the-art performance and a favorable accuracy-efficiency trade-off, supporting structurepreserving inference directly in ERP space as a promising alternative to projection-heavy panoramic pipelines.

Problem

• 360 SOD
• equirectangular projection
• spatial distortion
• panoramic geometry
• salient object structure

Innovation

• geometry-structure coupling
• deformable sampling
• context aggregation
• progressive refinement