Render-FM: A Foundation Model for Real-time Photorealistic Volumetric Rendering

• URL: http://arxiv.org/abs/2505.17338v1
• Date: Thu, 22 May 2025 23:18:30 GMT
• Title: Render-FM: A Foundation Model for Real-time Photorealistic Volumetric Rendering
• Authors: Zhongpai Gao, Meng Zheng, Benjamin Planche, Anwesa Choudhuri, Terrence Chen, Ziyan Wu,
• Abstract summary: We propose Render-FM, a novel foundation model for direct, real-time rendering of CT scans.<n>Our approach generates high-quality, real-time interactive 3D visualizations across diverse clinical CT data.<n>Experiments demonstrate that Render-FM achieves visual fidelity comparable or superior to specialized per-scan methods.
• Score: 28.764513004699676
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Volumetric rendering of Computed Tomography (CT) scans is crucial for visualizing complex 3D anatomical structures in medical imaging. Current high-fidelity approaches, especially neural rendering techniques, require time-consuming per-scene optimization, limiting clinical applicability due to computational demands and poor generalizability. We propose Render-FM, a novel foundation model for direct, real-time volumetric rendering of CT scans. Render-FM employs an encoder-decoder architecture that directly regresses 6D Gaussian Splatting (6DGS) parameters from CT volumes, eliminating per-scan optimization through large-scale pre-training on diverse medical data. By integrating robust feature extraction with the expressive power of 6DGS, our approach efficiently generates high-quality, real-time interactive 3D visualizations across diverse clinical CT data. Experiments demonstrate that Render-FM achieves visual fidelity comparable or superior to specialized per-scan methods while drastically reducing preparation time from nearly an hour to seconds for a single inference step. This advancement enables seamless integration into real-time surgical planning and diagnostic workflows. The project page is: https://gaozhongpai.github.io/renderfm/.

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