Abstract:
Traditional 3-D Gaussian-splatting (3DGS) modeling relies on iterative optimization, leading to substantial latency and limited generalizability. Feedforward 3DGS alleviates these limitations by generating Gaussians through a single network inference. However, most existing processors are optimized for optimization-based 3DGS and do not fully exploit the distinct computational characteristics of feedforward 3DGS. …