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.
在本文中，我们探讨了多模态输入对基于3D高斯分布（3D Gaussian Splatting, 3DGS）的辐射场渲染的能力。我们提出了一种新的方法——LiDAR-3DGS，通过将LiDAR生成的点云与3DGS输入相结合，显著提升了三维模型的准确性和细节表现。我们展示了一种系统性的LiDAR增强3DGS的方法，使其能够捕捉重要特征，例如螺栓、开口以及其他图像特征往往遗漏的细节。这些细节对于远程监控和维护等工程应用至关重要。我们在不修改3DGS底层算法的前提下，证明了即使适量加入LiDAR生成的点云，也能显著提升模型的感知质量。在30k次迭代后，使用我们方法生成的模型分别在峰值信噪比（PSNR）和结构相似性（SSIM）上提升了7.064%和0.565%。由于本研究中使用的LiDAR设备是常用的商用级设备，因此提升效果相对温和，若使用更高端的LiDAR系统，这些改进可进一步增强。此外，这些改进可以作为辐射场渲染其他衍生工作的补充，并为未来LiDAR与计算机视觉集成建模提供了新的见解。