English(EN) PanoSAMic: Panoramic Image Segmentation from SAM Feature Encoding and Dual View Fusion

PanoSAMic模型利用SAM特征增强全景图像分割

作者 PulseAugur 编辑部 · [1 个来源] · 2026-04-27 04:00

研究人员开发了PanoSAMic，一种利用预训练的Segment Anything (SAM)模型进行全景图像分割的新方法。该方法将SAM的编码器改编为输出多阶段特征，并包含一个用于选择相关模态和特征的融合模块。该系统在其解码器中利用球形注意力和双视图融合来解决全景图像中常见的失真和边缘不连续问题。PanoSAMic在Stanford2D3DS和Matterport3D等基准测试中，在各种数据模态上都展现出了最先进的性能。 AI

影响通过将基础模型改编为专用应用，增强了全景图像分析能力。

排序理由这是一篇描述图像分割新方法的学术论文。

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arXiv cs.CV TIER_1 English(EN) · Mahdi Chamseddine, Didier Stricker, Jason Rambach · 2026-04-27 04:00

PanoSAMic: 基于SAM特征编码和双视图融合的全景图像分割

arXiv:2601.07447v3 Announce Type: replace Abstract: Existing image foundation models are not optimized for spherical images having been trained primarily on perspective images. PanoSAMic integrates the pre-trained Segment Anything (SAM) encoder to make use of its extensive traini…

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PanoSAMic: 基于SAM特征编码和双视图融合的全景图像分割

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