GaussianPU：一种基于3D高斯泼溅的混合2D-3D上采样框架，用于增强彩色点云
GaussianPU: A Hybrid 2D-3D Upsampling Framework for Enhancing Color Point Clouds via 3D Gaussian Splatting
AI总结：
本文提出GaussianPU框架，利用3D高斯泼溅结合2D渲染图像恢复网络，在单GPU上实现无需分割的百万级稠密彩色点云上采样，显著提升机器人感知中点云质量。
摘要：
稠密彩色点云能够增强视觉感知，在各类机器人应用中具有重要价值。然而，现有的基于学习的点云上采样方法受限于计算资源和批处理策略，通常需要将点云细分为较小的块，导致产生失真并降低感知质量。为应对这一挑战，我们提出了一种新颖的基于3D高斯泼溅（3DGS）的2D-3D混合彩色点云上采样框架（GaussianPU），用于机器人感知。该方法利用3DGS在机器人视觉系统中桥接3D点云与其2D渲染图像。一个双尺度渲染图像恢复网络将稀疏点云渲染结果转换为稠密表示，随后将其与精确的机器人相机位姿以及插值后的稀疏点云一起输入3DGS，以重建稠密的3D点云。我们对原始3DGS进行了一系列增强，实现了对点数量的精确控制，并显著提升了用于机器人场景理解的上采样点云质量。我们的框架支持在单个消费级GPU（如NVIDIA GeForce RTX 3090）上处理完整点云，无需进行分割，从而为机器人导航和操作任务生成包含数百万点的高质量稠密彩色点云。在生成百万级点云数据方面的大量实验结果验证了我们方法的有效性，显著提高了彩色点云的质量，并展示了其在自主机器人和人机交互场景中大规模点云应用方面的巨大潜力。
英文摘要：
Dense colored point clouds enhance visual perception and are of significant value in various robotic applications. However, existing learning-based point cloud upsampling methods are constrained by computational resources and batch processing strategies, which often require subdividing point clouds into smaller patches, leading to distortions that degrade perceptual quality. To address this challenge, we propose a novel 2D-3D hybrid colored point cloud upsampling framework (GaussianPU) based on 3D Gaussian Splatting (3DGS) for robotic perception. This approach leverages 3DGS to bridge 3D point clouds with their 2D rendered images in robot vision systems. A dual scale rendered image restoration network transforms sparse point cloud renderings into dense representations, which are then input into 3DGS along with precise robot camera poses and interpolated sparse point clouds to reconstruct dense 3D point clouds. We have made a series of enhancements to the vanilla 3DGS, enabling precise control over the number of points and significantly boosting the quality of the upsampled point cloud for robotic scene understanding. Our framework supports processing entire point clouds on a single consumer-grade GPU, such as the NVIDIA GeForce RTX 3090, eliminating the need for segmentation and thus producing high-quality, dense colored point clouds with millions of points for robot navigation and manipulation tasks. Extensive experimental results on generating million-level point cloud data validate the effectiveness of our method, substantially improving the quality of colored point clouds and demonstrating significant potential for applications involving large-scale point clouds in autonomous robotics and human-robot interaction scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
