🤖 AI Summary
Rendering high-fidelity color images directly from static LiDAR scan point clouds remains challenging due to severe artifacts. This paper proposes a real-time neural rendering method that requires neither scene-specific training nor heavy preprocessing. Our approach introduces a synergistic framework combining a depth-guided U-Net and a depth-aware heuristic pre-filter: the U-Net performs end-to-end mapping via depth-guided point cloud projection, while the pre-filter suppresses artifacts without requiring registration ground truth. Leveraging synthetic data augmentation and GPU-optimized inference, the method achieves >30 FPS rendering on commodity GPUs. To our knowledge, this is the first neural point cloud renderer achieving robustness, high fidelity, and generalizability without any ground-truth annotations. Quantitative and qualitative evaluations demonstrate superior rendering quality and speed over state-of-the-art methods.
📝 Abstract
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.