混合视网膜路径追踪与边缘高斯分布用于沉浸式解剖学
Hybrid Foveated Path Tracing with Peripheral Gaussians for Immersive Anatomy
AI总结：
本文提出了一种结合视网膜路径追踪和高斯点分布的混合渲染方法，用于实现高质量、交互式和沉浸式的解剖学可视化。
摘要：
体积分层医学成像为理解复杂病理提供了巨大潜力。然而，传统2D切片对解释空间关系帮助有限，迫使用户必须在三维空间中手动重建解剖结构。直接体积分层路径追踪和VR渲染可以提高感知能力，但计算成本较高，而预先计算的表示方法，如高斯点分布，需要提前规划。两种方法都限制了交互使用。我们提出了一种混合渲染方法，用于高质量、交互式和沉浸式的解剖学可视化。我们的方法结合了流式视网膜路径追踪和轻量级高斯点分布对边缘的近似。边缘模型的生成通过体积数据优化，并通过视网膜渲染持续优化，从而实现交互式更新。基于深度的重新投影进一步提高了对延迟的鲁棒性，并允许用户在保真度与刷新率之间进行平衡。我们比较了我们的方法与直接路径追踪和高斯点分布。我们的结果突显了它们的结合如何在保持视觉质量的同时，能够在不到1秒内重新生成边缘模型，从而消除大量的预处理和近似。这为交互式医学可视化开辟了新的可能性。
英文摘要：
Volumetric medical imaging offers great potential for understanding complex pathologies. Yet, traditional 2D slices provide little support for interpreting spatial relationships, forcing users to mentally reconstruct anatomy into three dimensions. Direct volumetric path tracing and VR rendering can improve perception but are computationally expensive, while precomputed representations, like Gaussian Splatting, require planning ahead. Both approaches limit interactive use. We propose a hybrid rendering approach for high-quality, interactive, and immersive anatomical visualization. Our method combines streamed foveated path tracing with a lightweight Gaussian Splatting approximation of the periphery. The peripheral model generation is optimized with volume data and continuously refined using foveal renderings, enabling interactive updates. Depth-guided reprojection further improves robustness to latency and allows users to balance fidelity with refresh rate. We compare our method against direct path tracing and Gaussian Splatting. Our results highlight how their combination can preserve strengths in visual quality while re-generating the peripheral model in under a second, eliminating extensive preprocessing and approximations. This opens new options for interactive medical visualization.
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