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English(EN) RenderFormer++: Scalable and Physically Grounded Feed-Forward Neural Rendering

RenderFormer++ 通过物理基础和可扩展性推进神经渲染

研究人员推出 RenderFormer++,一个先进的前馈神经渲染框架,专为网格场景中的全局光照而设计。该新框架通过引入物理信息传输引导 (PITG) 来确保光传输的物理一致性,从而解决了先前基于 Transformer 的方法的局限性。此外,它利用分层以对象为中心的标记化 (HOCT) 通过将三角形级别的特征聚合到对象级别的标记中来提高可扩展性和效率。实验表明,RenderFormer++ 在渲染复杂、大规模场景方面提供了改进的物理准确性和效率。 AI

影响 该框架可能导致计算机图形学和人工智能驱动的视觉应用中更高效、物理上更准确的渲染。

排序理由 该集群包含一篇详细介绍神经渲染新方法的 ist 研究论文。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

RenderFormer++ 通过物理基础和可扩展性推进神经渲染

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Huangsheng Du, Haoran Zhu, Youcheng Cai, Jinyang Meng, Ligang Liu ·

    RenderFormer++: 可扩展且物理基础的馈式神经网络渲染

    arXiv:2606.30380v1 Announce Type: cross Abstract: We present RenderFormer++, a scalable and physically grounded feed-forward neural rendering framework for global illumination in mesh scenes. Existing Transformer-based neural rendering methods such as RenderFormer achieve promisi…

  2. arXiv cs.LG TIER_1 English(EN) · Ligang Liu ·

    RenderFormer++: 可扩展且物理上可信的前馈神经渲染

    We present RenderFormer++, a scalable and physically grounded feed-forward neural rendering framework for global illumination in mesh scenes. Existing Transformer-based neural rendering methods such as RenderFormer achieve promising cross-scene generalization, but suffer from lim…