Abstract
Anatomical labels help 3D Gaussian splatting preserve vessels in cinematic renderings of CT, but the representation is still found by optimising each patient's scene, where densification decides where Gaussians go, while real-time displays cap their number. We address one part of this process: deciding where a fixed budget of B Gaussians should be placed. We propose a reusable, labelaware allocation front end that maps a patient's CT, labels and transfer function to a density over 2mm cells and draws exactly B positions from it without per-patient optimisation; the other Gaussian attributes are still fitted per patient. The front end is learned once from per-patient optimised scenes and implemented as a population-trained neural prior and an interpretable seven-parameter visibility-aware closed form (VACF). With identical attribute fitting on 20 held-out patients at 10K-100K Gaussians, both raise vessel-masked PSNR over uniform allocation by 4.5-7.1dB and over pruning a dense voxel-aligned model by 1.6-3.3dB. The neural prior does not differ significantly from per-patient optimised allocation at any budget, and VACF is within 0.6dB of it on vessels; on 19 external patients the neural prior again matches it on vessels and VACF is within 0.7dB. As an initialisation for position optimisation, VACF allocation beats per-patient densification on vessels at 10K Gaussians and, at 25K, reaches a higher vessel PSNR after 118s of optimisation than densification after 248s. Under altered rendering presets, VACF transfers to new colours and lighting but not to a label-free transfer function, whereas the neural prior shows the opposite pattern.
Abstract (translated)
URL
https://arxiv.org/abs/2610.20397
