3 accepted papers
Recent 3D Gaussian Splatting (3DGS) dropout methods address overfitting under sparse-view conditions by randomly nullifying Gaussian opacities. However, we identify a neighbor compensation effect in these approaches: dropped Gaussians are often compensated by their neighbors, weakening the intended
While significant progress has been made in large-scale scene representation using Neural Radiance Fields (NeRF), several limitations remain. For instance, most methods still rely on the original coarse-to-fine sampling strategy, leading to an inefficient rendering process. Additionally, to model la…
We introduce NeRF-GS, a novel framework that jointly optimizes Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS). This framework leverages the inherent continuous spatial representation of NeRF to mitigate several limitations of 3DGS, including sensitivity to Gaussian initialization, li…