用激光雷达辅助预先密集化，提升3D高斯泼溅的效率与质量
DensifyBeforehand: LiDAR-assisted Content-aware Densification for Efficient and Quality 3D Gaussian Splatting
- 结合激光雷达与单目深度图，预先构建密集点云
- 在关键区域优先采样，减少冗余高斯分布
- 训练更快更省资源，适合复杂场景重建
本文针对现有3D高斯泼溅（3DGS）方法依赖自适应密度控制导致的浮动物体和资源浪费问题，提出一种新的‘预先密集化’方法。该方法通过融合稀疏激光雷达数据与对应彩色图像的单目深度估计，实现场景初始结构的高质量构建。采用感兴趣区域（ROI）感知采样策略，优先处理语义和几何重要区域，生成更密集且精准的点云，从而提升视觉保真度与计算效率。该方法跳过原始流程中可能引入冗余高斯项的自适应密度控制阶段，使优化聚焦于高斯原语的其他属性，降低重叠并增强视觉质量。在四个新收集的数据集上进行大量对比与消融实验验证，结果表明本方法在保持与最先进水平相当效果的同时，显著降低资源消耗与训练时间，有效保留复杂场景中的关键区域。
原文摘要 · Abstract (English)
This paper addresses the limitations of existing 3D Gaussian Splatting (3DGS) methods, particularly their reliance on adaptive density control, which can lead to floating artifacts and inefficient resource usage. We propose a novel densify beforehand approach that enhances the initialization of 3D scenes by combining sparse LiDAR data with monocular depth estimation from corresponding RGB images. Our ROI-aware sampling scheme prioritizes semantically and geometrically important regions, yielding a dense point cloud that improves visual fidelity and computational efficiency. This densify beforehand approach bypasses the adaptive density control that may introduce redundant Gaussians in the original pipeline, allowing the optimization to focus on the other attributes of 3D Gaussian primitives, reducing overlap while enhancing visual quality. Our method achieves comparable results to state-of-the-art techniques while significantly lowering resource consumption and training time. We validate our approach through extensive comparisons and ablation studies on four newly collected datasets, showcasing its effectiveness in preserving regions of interest in complex scenes.
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