Abstract
Recent advances in novel view synthesis (NVS) have enabled real-time rendering with 3D Gaussian Splatting (3DGS). However, existing methods struggle with artifacts and missing regions when rendering from viewpoints that deviate from the training trajectory, limiting seamless scene exploration. To address this, we propose a 3DGS-based pipeline that generates additional training views to enhance reconstruction. We introduce an information-gain-driven virtual camera placement strategy to maximize scene coverage, followed by video diffusion priors to refine rendered results. Fine-tuning 3D Gaussians with these enhanced views significantly improves reconstruction quality. To evaluate our method, we present Wild-Explore, a benchmark designed for challenging scene exploration. Experiments demonstrate that our approach outperforms existing 3DGS-based methods, enabling high-quality, artifact-free rendering from arbitrary viewpoints.
Overview
Overview of the proposed framework for scene exploration. (a) The scene is initially optimized using 3D Gaussian Splatting (3DGS) on the given training viewpoints. (b) Based on the optimized 3DGS and training viewpoints, we generate virtual camera trajectories, and enhance the rendered views using our diffusion-based enhancement model. (c) Finally, the scene is further optimized using both the original training viewpoints and the newly generated virtual viewpoints. See our paper for more details.
