LineGS : 3D Line Segment Representation on 3D Gaussian Splatting

TL;DR

LineGS addresses instability in 3D line reconstruction caused by errors in 2D multi-view matching and noise in 3D point clouds by integrating geometry-guided line reconstruction with a 3D Gaussian splatting model. It post-processes geometry-derived line segments by aligning them with the distribution of Gaussian centers, using cylinder-based Gaussian density, linear translation, cropping, and clustering-based refinement. The authors evaluate on multiple datasets and demonstrate improvements in representational accuracy and compactness, as indicated by reductions in $E_{rms}$ and increases in the representational Score across diverse scenes. This lightweight, implementable approach provides a robust abstract representation of 3D scenes suitable for mapping, localization, and rendering tasks.

Abstract

Abstract representations of 3D scenes play a crucial role in computer vision, enabling a wide range of applications such as mapping, localization, surface reconstruction, and even advanced tasks like SLAM and rendering. Among these representations, line segments are widely used because of their ability to succinctly capture the structural features of a scene. However, existing 3D reconstruction methods often face significant challenges. Methods relying on 2D projections suffer from instability caused by errors in multi-view matching and occlusions, while direct 3D approaches are hampered by noise and sparsity in 3D point cloud data. This paper introduces LineGS, a novel method that combines geometry-guided 3D line reconstruction with a 3D Gaussian splatting model to address these challenges and improve representation ability. The method leverages the high-density Gaussian point distributions along the edge of the scene to refine and optimize initial line segments generated from traditional geometric approaches. By aligning these segments with the underlying geometric features of the scene, LineGS achieves a more precise and reliable representation of 3D structures. The results show significant improvements in both geometric accuracy and model compactness compared to baseline methods.

Figures (8)

• Figure 1: Distribution of trained Gaussian points. The centers of Gaussian points are concentrated at the boundaries of pixel colors and preserve intact three-dimensional spatial information. This scene is $\mathcal{P}$LAYROOM from the Deep Blending dataset hedman2018deep.
• Figure 2: The visualization of these issues. Given a trick example, constructed using Blender, arranges multiple quadrilaterals together and renders 100 images around the center of the scene
• Figure 3: It shows the defination of $C(\Vec{s}, r)$ and the calculation of similarity between two line segments $\Vec{s_i}$ and $\Vec{s_j}$.
• Figure 4: The binary search cropping strategy of single segment $\Vec{s_i}$, and merge-join strategy of two segments $\Vec{s_i}$, $\Vec{s_j}$ within one cluster.
• Figure 5: Qualitative results on ABC-NEF ye2023nef. The proposed method accurately addresses issues, such as redundant segments and misalignment with the actual boundaries of Gaussians. While it may sacrifice some boundary details, this trade-off is acceptable for the Gaussians.