Abstract
Panoramic semantic segmentation is pivotal for comprehensive 360° scene understanding in critical applications like autonomous driving and virtual reality. However, progress in this domain is constrained by two key challenges: the severe geometric distortions inherent in panoramic projections and the prohibitive cost of dense annotation. While Unsupervised Domain Adaptation (UDA) from label-rich pinhole-camera datasets offers a viable alternative, many real-world tasks impose a stricter source-free (SFUDA) constraint where source data is inaccessible for privacy or proprietary reasons. This constraint significantly amplifies the core problems of domain shift, leading to unreliable pseudo-labels and dramatic performance degradation, particularly for minority classes. To overcome these limitations, we propose the DAPASS framework. DAPASS introduces two synergistic modules to robustly transfer knowledge without source data. First, our Panoramic Confidence-Guided Denoising (PCGD) module generates high-fidelity, class-balanced pseudo-labels by enforcing perturbation consistency and incorporating neighborhood-level confidence to filter noise. Second, a Contextual Resolution Adversarial Module (CRAM) explicitly addresses scale variance and distortion by adversarially aligning fine-grained details from high-resolution crops with global semantics from low-resolution contexts. DAPASS achieves state-of-the-art performances on outdoor (Cityscapes-to-DensePASS) and indoor (Stanford2D3D) benchmarks, yielding 55.04% (+2.05%) and 70.38% (+1.54%) mIoU, respectively.
Abstract (translated)
全景语义分割对于自动驾驶和虚拟现实等关键应用中的全方位360°场景理解至关重要。然而，该领域的发展受限于两个核心挑战：全景投影固有的严重几何畸变，以及密集标注的高昂成本。尽管从标注丰富的针孔相机数据集进行无监督域适应（UDA）提供了一种可行方案，但许多实际任务施加了更严格的源自由（SFUDA）约束——因隐私或专有原因无法访问源数据。这一约束显著加剧了域偏移的核心问题，导致伪标签不可靠且性能急剧下降，尤其对少数类影响显著。为克服这些限制，我们提出了DAPASS框架。该框架引入两个协同模块以在无源数据条件下稳健迁移知识：首先，全景置信度引导去噪（PCGD）模块通过强制扰动一致性并引入邻域级置信度过滤噪声，生成高保真、类别平衡的伪标签；其次，上下文分辨率对抗模块（CRAM）通过对抗性对齐高分辨率裁剪的细粒度细节与低分辨率上下文的全局语义，显式解决尺度变异和畸变问题。DAPASS在户外（Cityscapes-to-DensePASS）和室内（Stanford2D3D）基准测试中均达到最先进性能，分别取得55.04%（+2.05%）和70.38%（+1.54%）的mIoU。
URL
https://arxiv.org/abs/2603.25131
