3D Gaussian Splatting within Self-Learned Neural View-Dependent Color Fields

3D Gaussian Splatting (3DGS) has become a vital tool in novel view synthesis. 3D Gaussian functions with attributes like color, opacity, and scale are stacked together to explicitly model a radiance field. However, discrete 3D Gaussians usually produce poor depth with few geometry details but lots of floaters, which makes modeling smooth radiance fields remain a challenge. To address this issue, we propose to learn a radiance field within a continuous view-dependent color field parameterized by neural networks. With the continuous constraints in color, the bias on low-frequency signals of neural networks highly encourages the geometry to contribute to high-frequency color variations on images, but not solely relying on the color attribute itself. Moreover, we impose a constraint to improve the Lipschitz continuity of the neural color field, making large changes of each Gaussian's color also match with large changes of its position in terms of a ratio. To this end, we additionally introduce novel self-learning strategies to learn neural view-dependent color fields without any external knowledge or priors, aiming for better generalization. Our numerical and visual comparisons on widely used benchmarks justify our idea and show better ability of high fidelity geometry recovery in 3DGS.