DA360模型通过尺度不变性增强360度深度估计

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 04:00

研究人员开发了DA360，这是Depth Anything V2模型的全景适应版本，用于改进360度深度估计。该新框架利用了现有的DAV2模型的零样本泛化能力，并采用了轻量级适应过程。DA360从ViT类令牌中学习每张图像的偏移量，以实现尺度不变监督，并将循环填充集成到DPT解码器中以消除接缝伪影，从而获得更准确的3D点云和更好的空间一致性。 AI

影响通过提高全景深度估计的准确性，增强了机器人和AR/VR应用的三维场景理解能力。

排序理由这是一篇详细介绍全景深度估计新模型适应性的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Hualie Jiang, Ziyang Song, Zhiqiang Lou, Rui Xu, Minglang Tan · 2026-10-01 04:00

Depth Anything in $360^\circ$: Towards Scale Invariance in the Wild

arXiv:2512.22819v2 Announce Type: replace Abstract: Panoramic depth estimation captures the complete 360$^\circ$ scene geometry, being essential for robotics and AR/VR applications. While perspective depth models have achieved remarkable zero-shot generalization via large-scale t…

本文如何被排名

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Depth Anything in $360^\circ$: Towards Scale Invariance in the Wild

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