Render-FM: 用于实时逼真体积渲染的前馈模型
Render-FM: Feedforward Model for Real-time Photorealistic Volumetric Rendering
AI总结：
提出Render-FM前馈模型，直接从CT体积回归6D高斯溅射参数，实现2.8秒实时渲染，速度提升500倍，并引入解剖引导初始化提升医学图像渲染质量。
摘要：
CT扫描的逼真体积渲染极大地有益于临床工作流程，但神经辐射场（NeRF）和3D高斯溅射（3DGS）等神经方法需要每次扫描进行耗时的优化（NeRF数小时，3DGS约30分钟），使其在临床环境中不实用。我们提出Render-FM，一种前馈模型，通过单次2.8秒前向传播直接从CT体积回归6D高斯溅射（6DGS）参数，消除了这一瓶颈，速度比每次扫描优化快500倍。为了弥合自然场景重建与医学体积渲染之间的领域差距，我们引入了解剖引导初始化（AGP），它将分割掩码和传递函数作为结构和外观先验，这些信息是现有高斯溅射方法所忽略的。Render-FM基于受nnU-Net启发的3D U-Net，在多样化的CT扫描上训练，预测每个体素的6DGS参数，并支持即时实时渲染。与每次扫描的方法不同，它能泛化到未见过的解剖结构、新颖的传递函数，并实现组合器官可视化，无需额外的准备时间。可选的89秒微调进一步提高质量，超越每次扫描优化的基线。项目页面：此 https URL。
英文摘要：
Photorealistic volumetric rendering of CT scans greatly benefits clinical workflows, yet neural approaches such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) require prohibitive per-scan optimization (hours for NeRF, about 30 minutes for 3DGS), making them impractical in clinical settings. We propose Render-FM, a feedforward model that eliminates this bottleneck by directly regressing 6D Gaussian Splatting (6DGS) parameters from a CT volume in a single 2.8-second forward pass, a 500x speedup over per-scan optimization. To bridge the domain gap between natural scene reconstruction and medical volumetric rendering, we introduce Anatomy-Guided Priming (AGP), which incorporates segmentation masks and transfer functions as structural and appearance priors, information that existing Gaussian splatting methods overlook. Built on an nnU-Net-inspired 3D U-Net trained on diverse CT scans, Render-FM predicts per-voxel 6DGS parameters and supports immediate real-time rendering. Unlike per-scan methods, it generalizes to unseen anatomies, novel transfer functions, and enables compositional organ visualization with zero additional preparation time. Optional 89-second fine-tuning further improves quality, surpassing per-scan optimized baselines. Project page: https://gaozhongpai.github.io/renderfm/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
