用激光雷达点云增强3D高斯溅射，让模型细节更真实。
LiDAR-3DGS: LiDAR Reinforced 3D Gaussian Splatting for Multimodal Radiance Field Rendering
- 融合激光雷达点云与图像，提升3D建模精度。
- 30k次迭代后PSNR提升7.064%，SSIM提升0.565%。
- 适合需要高精度建模的工程监测与维护场景。
本文探索多模态输入在基于3D高斯溅射（3DGS）的辐射场渲染中的能力。提出LiDAR-3DGS方法，通过激光雷达生成的点云强化3DGS输入，显著提升3D模型的准确性和细节表现。该方法系统性地引入激光雷达信息，有效捕捉螺栓、孔洞等常被纯图像特征忽略的关键结构，对远程监控与维护等工程应用至关重要。在不修改原有3DGS算法的前提下，仅添加少量激光雷达点云，即可明显提升模型感知质量。在30k次迭代时，模型的PSNR提升7.064%，SSIM提升0.565%。所用激光雷达为常见商用设备，性能提升尚有潜力，未来更高精度设备可带来更大改进。该方法亦可与其他辐射场渲染衍生工作结合，为激光雷达与计算机视觉融合建模提供新思路。
原文摘要 · Abstract (English)
In this paper, we explore the capabilities of multimodal inputs to 3D Gaussian Splatting (3DGS) based Radiance Field Rendering. We present LiDAR-3DGS, a novel method of reinforcing 3DGS inputs with LiDAR generated point clouds to significantly improve the accuracy and detail of 3D models. We demonstrate a systematic approach of LiDAR reinforcement to 3DGS to enable capturing of important features such as bolts, apertures, and other details that are often missed by image-based features alone. These details are crucial for engineering applications such as remote monitoring and maintenance. Without modifying the underlying 3DGS algorithm, we demonstrate that even a modest addition of LiDAR generated point cloud significantly enhances the perceptual quality of the models. At 30k iterations, the model generated by our method resulted in an increase of 7.064% in PSNR and 0.565% in SSIM, respectively. Since the LiDAR used in this research was a commonly used commercial-grade device, the improvements observed were modest and can be further enhanced with higher-grade LiDAR systems. Additionally, these improvements can be supplementary to other derivative works of Radiance Field Rendering and also provide a new insight for future LiDAR and computer vision integrated modeling.
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