用U-Net实时渲染带瑕疵的LiDAR点云，生成逼真图像。
Real-time Neural Rendering of LiDAR Point Clouds
- 用U-Net加深度启发式预处理，实现点云到图像的高效转换。
- 在消费级GPU上实现实时渲染，质量优于现有方法。
- 无需训练特定场景模型，适合快速可视化真实点云数据。
静态LiDAR扫描器生成高精度、稠密且带颜色的点云，但常含明显伪影，难以直接显示。本文提出一种高效方法，在无需昂贵预处理或训练特定场景模型的前提下，实现点云的逼真图像渲染。直接使用1×1像素投影虽快且保留细节，但导致背景点渗入前景像素，图像失真。关键思路是通过深度卷积网络（U-Net）和基于深度的启发式预过滤，将此类投影转化为真实感结果。U-Net还能解决LiDAR特有的缺失区域、颜色不一致及点密度差异问题。此外，提出合成训练数据的方法以应对未对齐的真实图像。该方法在消费级GPU上实现实时渲染，速度与质量均超越当前最优方案。
原文摘要 · Abstract (English)
Static LiDAR scanners produce accurate, dense, colored point clouds, but often contain obtrusive artifacts which makes them ill-suited for direct display. We propose an efficient method to render photorealistic images of such scans without any expensive preprocessing or training of a scene-specific model. A naive projection of the point cloud to the output view using 1x1 pixels is fast and retains the available detail, but also results in unintelligible renderings as background points leak in between the foreground pixels. The key insight is that these projections can be transformed into a realistic result using a deep convolutional model in the form of a U-Net, and a depth-based heuristic that prefilters the data. The U-Net also handles LiDAR-specific problems such as missing parts due to occlusion, color inconsistencies and varying point densities. We also describe a method to generate synthetic training data to deal with imperfectly-aligned ground truth images. Our method achieves real-time rendering rates using an off-the-shelf GPU and outperforms the state-of-the-art in both speed and quality.
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