GDGS: Gradient Domain Gaussian Splatting for Sparse Representation of Radiance Fields

arXiv:2405.05446v1 Announce Type: cross
Abstract: The 3D Gaussian splatting methods are getting popular. However, they work directly on the signal, leading to a dense representation of the signal. Even with some techniques such as pruning or distillation, the results are still dense. In this paper, we propose to model the gradient of the original signal. The gradients are much sparser than the original signal. Therefore, the gradients use much less Gaussian splats, leading to the more efficient storage and thus …

abstract arxiv cs.ai cs.cv cs.gr cs.lg distillation domain eess.iv fields gradient however paper popular pruning representation results signal type work