MLP Splatting: Object-Centric Neural Fields

3D representations are fundamental for scene rendering, un-derstanding, and interaction. Approaches like Neural Radiation Fieldsand 3D Gaussian Splatting achieve impressive photorealistic novel-viewsynthesis, but lack the ability to easily decompose scene elements intoobject level, requiring additional segmentation or grouping. We presentMLP-Splatting, a method that enables scene decomposition via a fewexpressive light-field primitives while providing photorealistic novel-viewsynthesis.MLP-Splatting models each primitive as an independent compact MLPwith localized spatial support that predicts radiance and opacity. In con-trast to low-level Gaussian primitives or a single global radiance field,our neural primitives provide greater expressive capacity while remain-ing spatially localized. Rendering is performed through efficient sparsevolumetric compositing over ray–primitive interactions.Our primitives are supervised using RGB supervision alone, which yieldsprimitives that represent local scene regions often corresponding to ob-jects or object parts, enabling interactive object-level editing withoutsegmentation masks by selecting a handful of primitives. Our method,augmented with optional semantic feature distillation, enables open-vocabulary scene interaction and open-set instant segmentation. Com-pared to state-of-the-art semantic 3DGS methods, we achieve substan-tially lower memory usage (1/7×) and faster rendering (5×).Project Page: https://shinjeongkim.com/mlp-splatting