arXiv Computer Vision By Yixiong Yang, Sisheng Zhang, Qingsong Yan, Shaohuai Shi, Qiang Wang

Laplacian Frequency Hierarchies for Efficient 3D Gaussian Splatting Training

Read the original on arXiv Computer Vision →

arXiv:2609. 03334v1 Announce Type: new Abstract: A key bottleneck in 3D Gaussian Splatting training is the continual growth of Gaussian primitives, which increases optimization cost and slows convergence, especially at high resolutions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

arXiv Computer Vision
Aug 28

ReSplat: Learning Recurrent Gaussian Splatting

ReSplat introduces a recurrent Gaussian splatting model that iteratively refines 3D Gaussians using the rendering error as a feedback signal, avoiding explicit gradient computation. The method starts from a compact reconstruction in a subsampled space, producing far fewer Gaussians than prior per‑pixel models, which reduces computational cost. Experiments on multiple datasets, view counts, and resolutions show state‑of‑the‑art performance with faster rendering speeds.

By Haofei Xu, Daniel Barath, Andreas Geiger, Marc Pollefeys