Application of 3D Gaussian Splatting for Cinematic Anatomy on Consumer Class Devices

Abstract: Interactive photorealistic rendering of 3D anatomy is used in medical education to explain the structure of the human body. It is currently restricted to frontal teaching scenarios, where even with a powerful GPU and high-speed access to a large storage device where the data set is hosted, interactive demonstrations can hardly be achieved. We present the use of novel view synthesis via compressed 3D Gaussian Splatting (3DGS) to overcome this restriction, and to even enable students to perform cinematic anatomy on lightweight and mobile devices. Our proposed pipeline first finds a set of camera poses that captures all potentially seen structures in the data. High-quality images are then generated with path tracing and converted into a compact 3DGS representation, consuming < 70 MB even for data sets of multiple GBs. This allows for real-time photorealistic novel view synthesis that recovers structures up to the voxel resolution and is almost indistinguishable from the path-traced images

• The paper introduces a compressed 3D Gaussian splatting method to render detailed 3D anatomy with voxel-level fidelity.
• The technique achieves significant compression by reducing multi-gigabyte datasets to under 70 MB and rendering at 60 FPS on high resolutions.
• This advancement democratizes access to photorealistic 3D anatomy on consumer devices, enhancing medical education and immersive applications.

Novel View Synthesis for Cinematic Anatomy on Mobile and Immersive Displays

The research paper, "Novel View Synthesis for Cinematic Anatomy on Mobile and Immersive Displays," pioneers an advanced method of rendering interactive photorealistic visualization of 3D anatomy. This venture, aimed at enhancing the domain of medical education, embraces the application termed Cinematic Anatomy designed by Siemens Healthineers. Traditionally constrained by the need for powerful computing resources, this work strives to enable high-fidelity anatomy models to operate on lightweight mobile devices and within virtual reality (VR) environments.

Methodological Contributions

The paper introduces a sophisticated technique grounded in compressed 3D Gaussian splatting. Primarily, the implementation hinges on automatic image selection from the dataset, utilizing views that best capture anatomical structures under differential transfer function settings. Such an approach ensures that even intricate volumetric details are retained up to voxel-level fidelity. The incorporation of Mip-Splatting facilitates smoother transitions across varying focal lengths, addressing aliasing problems at different scales.

One highlighted numerical achievement is the significant data compression. For example, the paper demonstrates that datasets spanning several gigabytes can be condensed to less than 70 MB, thus ensuring smooth rendering ability on devices devoid of high-end GPUs. The real-time rendering speeds achieve an impressive performance of up to 60 frames per second at a resolution of 2048x2048 pixels, symbolizing a two-orders-of-magnitude increase over conventional path-tracing methods.

Implications: Practical and Theoretical

Practically, this advancement holds transformative potential for mobile and VR platforms in medical education. Breaking free from the erstwhile computing-intensive constraints, medical personnel and students are now afforded unparalleled portability and accessibility to high-resolution 3D anatomical data. This dynamic could substantially augment personalized learning experiences on immersive displays, meanwhile democratizing access to quality educational content irrespective of geographical and infrastructural limitations.

Theoretically, this work expands the scope of novel view synthesis beyond standard static scenes. By amalgamating evolving methodologies such as differentiable rendering and compressed representations, it pioneers a holistic approach toward handling large, real-world datasets with minimized computational overhead.

Discussion and Future Prospects

While the framework substantially diminishes the processing power requirement, certain limitations linger. The system inherently relies on predefined transfer functions, constraining exploratory tasks and interactive parameter modification by the end-user. The fixed lighting conditions within the Gaussian splat representation pose challenges to dynamically adjusting scene illumination. Furthermore, the current setup's handling of highly transparent datasets and scenes where exploratory viewing or real-time interaction with underlying parameters remains to be optimized.

Continued exploration of this methodology could involve addressing these limitations, particularly allowing dynamic adjustments to lighting and transfer functions in real-time operations. Additionally, there is potential for expanding this work to incorporate developments such as adaptive Gaussian splat strategies or machine learning-enhanced optimization schemes. Enhancing these areas could pave the way for smoother real-time interactions in VR environments, further enriching the fidelity and functionality of synthetic anatomy models.

The paper sets a compelling foundation that hints at an elevated platform for future developments, merging efficient computational strategies with practical educational advancements in the field of anatomy and perhaps broader medical applications.