MLP Splatting: Object-Centric Neural Fields

3D representations are fundamental for scene rendering, understanding, and interaction. Approaches like Neural Radiation Fields and 3D Gaussian Splatting achieve impressive photorealistic novel-view synthesis, but lack the ability to easily decompose scene elements into object level, requiring additional segmentation or grouping. We present MLP-Splatting, a method that enables scene decomposition via a few expressive light-field primitives while providing photorealistic novel-view synthesis. MLP-Splatting models each primitive as an independent compact MLP with localized spatial support that predicts radiance and opacity. In contrast to low-level Gaussian primitives or a single global radiance field, our neural primitives provide greater expressive capacity while remaining spatially localized. Rendering is performed through efficient sparse volumetric compositing over ray-primitive interactions. Our primitives are supervised using RGB supervision alone, which yields primitives that represent local scene regions often corresponding to objects or object parts, enabling interactive object-level editing without segmentation 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. Compared to state-of-the-art semantic 3DGS methods, we achieve substantially lower memory usage (1/7 times) and faster rendering (5 times). Project Page: https://shinjeongkim.com/mlp-splatting