arXiv:2505.17338v3 Announce Type: replace-cross Abstract: 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 optimizatio
Advances in AI, particularly in generative models and neural rendering, are enabling significant speedups in computationally intensive tasks.
This development can significantly accelerate clinical workflows by making photorealistic volumetric rendering of CT scans practical for real-time use, breaking a major bottleneck for technologies like NeRF and 3DGS.
The previous bottleneck of prohibitive per-scan optimization times for high-quality volumetric rendering is effectively eliminated, moving these techniques closer to widespread clinical adoption.
- · Medical imaging companies
- · Hospitals and radiology clinics
- · AI hardware manufacturers
- · Patients
- · Legacy medical imaging software companies that cannot adapt quickly
- · Researchers relying solely on slower, traditional optimization methods
Faster processing leads to more efficient diagnoses and treatment planning in medical settings.
This efficiency could facilitate the broader integration of advanced 3D visualization into routine clinical practice and surgical planning.
The underlying methodology might extend to other computationally intensive 3D rendering tasks beyond medicine, accelerating development in fields like industrial design or virtual reality.
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Read at arXiv cs.AI