TY - GEN
T1 - Poster Abstract
T2 - 23rd ACM Conference on Embedded Networked Sensor Systems, SenSys 2025
AU - Li, Wenba
AU - Jin, Zhanpeng
AU - Yang, Yuqin
N1 - Publisher Copyright:
© 2025 Copyright held by the owner/author(s).
PY - 2025/5/6
Y1 - 2025/5/6
N2 - 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.
AB - 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.
KW - dataset
KW - denoise
KW - lidar
KW - point cloud
KW - real sense
UR - https://www.scopus.com/pages/publications/105035750961
U2 - 10.1145/3715014.3724032
DO - 10.1145/3715014.3724032
M3 - Conference contribution
AN - SCOPUS:105035750961
T3 - ACM SenSys 2025 - 23rd ACM Conference on Embedded Networked Sensor Systems, In Transactions to Conference Embedded Artificial Intelligence and Sensing Systems
SP - 610
EP - 611
BT - ACM SenSys 2025 - 23rd ACM Conference on Embedded Networked Sensor Systems, In Transactions to Conference Embedded Artificial Intelligence and Sensing Systems
PB - Association for Computing Machinery, Inc
Y2 - 6 May 2025 through 9 May 2025
ER -