[Paper Review] Trim 3D Gaussian Splatting for Accurate Geometry Representation

Lue Fan, Yuxue Yang+3 more|arXiv (Cornell University)|Jun 11, 2024

Image Processing and 3D Reconstruction4 citations

TL;DR

This paper proposes Trim 3D Gaussian Splatting (TrimGS), a novel method that improves 3D geometry reconstruction by selectively trimming inaccurate or redundant Gaussians based on their contribution to rendering, while maintaining small Gaussian scales to preserve fine details. TrimGS achieves superior geometric accuracy and perceptual quality over baseline 3DGS and 2DGS methods by integrating contribution-based trimming, scale control, and normal consistency regularization.

ABSTRACT

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

Motivation & Objective

• To address the issue of messy, inaccurate geometry in 3D Gaussian Splatting (3DGS) despite high rendering quality.
• To propose a new strategy—Gaussian trimming—that refines 3D Gaussian fields by removing inaccurate or redundant components rather than relying solely on strong geometric regularization.
• To analyze the impact of Gaussian scale on geometry representation and optimization, revealing that large Gaussians hinder detail preservation and gradient stability.
• To develop a general-purpose method compatible with existing 3DGS and 2DGS frameworks that enhances both geometric accuracy and perceptual rendering quality.

Proposed method

• Introduces a contribution-based metric to evaluate each Gaussian’s influence on rendering, derived from alpha-blending weights, to guide trimming decisions.
• Proposes a gradual trimming strategy that removes Gaussians with low contribution while preserving those essential for accurate geometry.
• Implements scale control during densification to maintain relatively small Gaussian scales, improving high-frequency detail representation and reducing gradient noise.
• Integrates normal consistency regularization to stabilize geometry optimization and enhance surface accuracy.
• Designs a training pipeline that combines trimming, scale control, and regularization in a unified framework compatible with 3DGS and 2DGS.
• Employs a gamma-controlled thresholding mechanism (Eq. 5) to balance trimming aggressiveness and geometry fidelity.

Experimental results

Research questions

• RQ1 Can Gaussian trimming based on contribution evaluation improve 3D geometry reconstruction beyond traditional geometric regularization?
• RQ2 How does Gaussian scale affect the optimization process and detail representation in 3DGS?
• RQ3 Does maintaining small Gaussian scales lead to better preservation of fine geometric and appearance details?
• RQ4 To what extent can trimming be combined with existing regularization techniques like normal consistency without degrading rendering quality?
• RQ5 How does the proposed method compare to state-of-the-art 2DGS and 3DGS in terms of geometry accuracy and perceptual quality?

Key findings

• TrimGS reduces mean Chamfer Distance (CD) by up to 30% on the DTU dataset compared to baseline 3DGS, with CD values dropping from 1.45 to 0.82 under optimal trimming.
• On the MipNeRF360 benchmark, Trim2DGS achieves a 19.3% improvement in LPIPS (0.193 vs. 0.218) compared to 2DGS, indicating significantly better perceptual quality.
• Trim2DGS without scale control suffers a 15% increase in LPIPS (0.218 vs. 0.193), confirming that scale control is critical for preserving fine details.
• The method maintains high PSNR (e.g., 29.98 on MipNeRF360) while improving geometry accuracy, demonstrating a balance between rendering quality and geometric fidelity.
• Visualization shows that Trim2DGS produces more uniformly distributed Gaussians and reduces storage consumption, as seen in the flower and bicycle scenes.
• Theoretical and empirical analysis confirms that large Gaussians suffer from noisy gradients and limited capacity for high-frequency detail representation.