解耦球面推理与稠密预测以实现360度深度估计
Decoupling Spherical Reasoning from Dense Prediction for 360 Depth Estimation
- Beijing Jiaotong University（北京交通大学）
- Hefei University of Technology（合肥工业大学）
- Shandong University（山东大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对360度深度估计中ERP畸变问题，提出斐波那契球面图（FSG）进行球面推理，并用球面上下文条件化（SCC）模块调制稠密预测，在三个基准上取得更优深度精度。
AI中文摘要：
等距柱状投影（ERP）广泛用于全景深度估计，但其空间变化的畸变使得几何一致的特征建模具有挑战性。我们通过将原生球面空间中的上下文建模与稠密ERP预测解耦，重新审视全景深度估计。为此，我们提出斐波那契球面图（FSG）作为中间推理空间，将ERP特征提升到球面上的准均匀斐波那契节点，并通过互补的球面邻域捕获局部和长距离依赖。由此产生的球面离散化在球面上近似均匀地分布图节点，减少了关系建模中高度拉伸区域的过度表示。在紧凑的斐波那契节点集上操作也避免了在全ERP分辨率下构建和处理图的计算负担。为了桥接球面推理与稠密预测，我们提出球面上下文条件化（SCC）模块，该模块用增强的球面表示自适应地调制稠密ERP特征，使球面上下文能够引导像素对齐的深度预测。在三个基准上的大量实验表明，所提出的方法在深度准确性上持续优于现有方法。
英文摘要：
The equirectangular projection (ERP) is widely used for panoramic depth estimation, but its spatially varying distortion makes geometry-consistent feature modeling challenging. We revisit panoramic depth estimation by decoupling contextual modeling in native spherical space from dense ERP prediction. To this end, we propose a Fibonacci Spherical Graph (FSG) as an intermediate reasoning space to lift ERP features onto quasi-uniform Fibonacci nodes on the sphere and capture local and long-range dependencies through complementary spherical neighborhoods. The resulting spherical discretization distributes graph nodes approximately uniformly over the spherical surface, reducing the over-representation of highly stretched regions during relational modeling. Operating on a compact set of Fibonacci nodes also avoids the computational burden of constructing and processing a graph at full ERP resolution. To bridge spherical reasoning and dense prediction, we propose a Spherical Context Conditioning (SCC) module that adaptively modulates dense ERP features with the enhanced spherical representation, allowing spherical context to guide pixel-aligned depth prediction. Extensive experiments on three benchmarks demonstrate that the proposed method consistently achieves superior depth accuracy over existing approaches.
