Poster Abstract: R2R-LPCD: A Real-to-real Lidar Point Cloud Denoising Dataset

In recent years, low-cost LiDAR has gained attention for its cost-effectiveness, but the noisy point cloud data it captures limits algorithm performance. We propose the R2R-LPCD dataset, a real-world LiDAR-based point cloud denoising dataset designed to address the limitations of synthetic data in capturing complex noise patterns. Comprising 162 high-quality sample pairs, R2R-LPCD uniquely reflects real-world noise characteristics, such as ray-like noise at object boundaries and occlusion-induced structural gaps, offering a robust platform for algorithm evaluation under practical conditions. This dataset supports advancements in sensor systems, embedded AI, and real-world applications by providing tools and benchmarks for resource-efficient machine learning and edge computing. By publicly releasing R2R-LPCD, we aim to drive innovation in low-cost LiDAR applications, particularly in autonomous driving and robotics, while addressing current technical challenges through future scalability and methodological improvements.