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Marigold V2 使用扩散 Transformer 推进单目深度估计

研究人员开发了 Marigold V2,这是单目深度估计领域的一项进展,它重新利用了扩散 Transformer (DiT) 架构。该新方法通过采用预训练流匹配模型的单步推理、与真实特征的语义对齐以及使用基于 Sinkhorn 的损失的新颖两阶段微调协议,实现了更清晰、更详细的深度图。Marigold V2 在 KITTI 和 ETH3D 等基准测试中显示出比先前模型显著的改进,并且在表面法线估计和内在图像分解等相关任务上也表现出最先进的性能。 AI

影响 推进单目深度估计,可能改进机器人技术、场景重建和计算摄影领域的应用。

排序理由 该集群描述了一篇关于使用扩散 Transformer 在计算机视觉任务中取得进展的新研究论文。

在 arXiv cs.LG 阅读 →

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Marigold V2 使用扩散 Transformer 推进单目深度估计

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该集群描述了一篇关于使用扩散 Transformer 在计算机视觉任务中取得进展的新研究论文。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Igor Pavlovic, Thiemo Wandel, Anton Obukhov, Luca Bartolomei, Andrey Davydov, Fabio Tosi, Matteo Poggi, Sabine S\"usstrunk, Dengxin Dai ·

    Marigold V2:重新审视单目深度估计的扩散 Transformer

    arXiv:2609.08084v1 Announce Type: cross Abstract: Monocular depth estimation is a ubiquitous yet highly ill-posed computer vision task, with downstream applications in scene reconstruction, computational photography, and robotics, among others. Despite the field's maturity, recen…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Marigold V2:重新审视单目深度估计的扩散 Transformer

    Marigold V2 repurposes diffusion transformers for monocular depth estimation via single-step flow-matching inference, semantic alignment, and a Sinkhorn-based two-stage fine-tuning protocol, yielding sharper out-of-distribution depth maps and strong results on related dense regre…