Learning 4D Splatting Kernels

We present a differentiable framework for learning spatial and temporal kernels directly from data in a 4D splatting-based pipeline for dynamic novel-view synthesis. Each primitive is equipped with spatial and temporal latents. The spatial latent and time-conditioned 3D attributes are mapped to a radially symmetric 2D kernel through neural projection and decoding, while a separate decoder maps the temporal latent to a temporal kernel. The networks are jointly optimized with 4D primitive attributes, including the latents. The learned kernels adapt to scene structure and dynamics, exhibiting diverse profiles beyond Gaussian forms. The effectiveness of our approach is demonstrated on standard dynamic novel-view synthesis benchmarks, comparing favorably against state-of-the-art techniques.