Necromantic Gaussian: Raising the Retention Confidence for Continuous Level of Detail in 3D Gaussian Splatting

Level of detail (LoD) for 3D Gaussian Splatting trades rendering cost against fidelity, but reducing a trained field requires deciding which primitives to retain. Heuristics based on size, distance, or opacity can discard essential coverage, while hard selection removes the reconstruction feedback that could reverse a bad decision. We introduce Necromantic Gaussian, which learns retention confidence from the rendering loss itself. Built on Octree-GS, a lightweight router predicts per-anchor confidence from base and dedicated side features. Rendering supervision teaches which anchors are kept, while a direct count objective calibrates how many decoded Gaussians are selected at a requested budget. An extended rasterizer backward pass evaluates local reconstruction sensitivity at zero opacity, allowing rejected gates to receive image evidence without contributing to the hard forward image. Scale compensation and joint optimization of the base representation help the retained subset preserve coverage. The resulting confidence ordering lets a single checkpoint serve budgets specified at inference. We evaluate its quality–workload trade-offs on Tanks&Temples and Mip-NeRF 360, including controlled mechanism ablations.