  In this paper, we introduce Trim 3D Gaussian Splatting (TrimGS) to
reconstruct accurate 3D geometry from images. Previous arts for geometry
reconstruction from 3D Gaussians mainly focus on exploring strong geometry
regularization. Instead, from a fresh perspective, we propose to obtain
accurate 3D geometry of a scene by Gaussian trimming, which selectively removes
the inaccurate geometry while preserving accurate structures. To achieve this,
we analyze the contributions of individual 3D Gaussians and propose a
contribution-based trimming strategy to remove the redundant or inaccurate
Gaussians. Furthermore, our experimental and theoretical analyses reveal that a
relatively small Gaussian scale is a non-negligible factor in representing and
optimizing the intricate details. Therefore the proposed TrimGS maintains
relatively small Gaussian scales. In addition, TrimGS is also compatible with
the effective geometry regularization strategies in previous arts. When
combined with the original 3DGS and the state-of-the-art 2DGS, TrimGS
consistently yields more accurate geometry and higher perceptual quality. Our
project page is https://trimgs.github.io
