Measuring hierarchical spatial aggregation for three-dimensional perception under 38% point-cloud sparsity: a factor ablation
PDF

Keywords

three-dimensional perception
hierarchical spatial aggregation
point-cloud sparsity
paired simulation
reproducibility

Abstract

We evaluated hierarchical spatial aggregation for three-dimensional perception under 38% point-cloud sparsity. A deterministic paired simulation generated 64 cases and preserved a late-arriving block. Mean geometry recall changed from 0.519 to 0.571; the paired difference was +0.052 (95% interval +0.049 to +0.054). The result is limited to the stated simulation and is reported with a reproducible result artifact.

PDF

References

Sun, Y., Zia, A., Long, Z., Qiu, Z., Xiang, W., & Zhou, J. (2026). Hierarchical Spatial Mamba Framework for Point Cloud Classification. Lecture Notes in Computer Science, 402-417. https://doi.org/10.1007/978-981-95-4395-3_28

Morsy, S., & Shaker, A. (2022). Evaluation of LiDAR-Derived Features Relevance and Training Data Minimization for 3D Point Cloud Classification. Remote Sensing, 14(23), 5934. https://doi.org/10.3390/rs14235934

You, J., & Kim, Y. K. (2022). Up-Sampling Method for Low-Resolution LiDAR Point Cloud to Enhance 3D Object Detection in an Autonomous Driving Environment. Sensors, 23(1), 322. https://doi.org/10.3390/s23010322

Chen, C., Lan, J., Liu, H., Chen, S., & Wang, X. (2022). LiDAR--camera calibration based on back-projection of background point cloud. https://doi.org/10.22541/au.165451875.53371159/v1

Kulawiak, M. (2024). Comparison of 3D Point Cloud Completion Networks for High Altitude Lidar Scans of Buildings. Photogrammetric Engineering & Remote Sensing, 90(1), 55-64. https://doi.org/10.14358/pers.23-00056r2

Wang, Y., Weinacker, H., & Koch, B. (2008). A Lidar Point Cloud Based Procedure for Vertical Canopy Structure Analysis And 3D Single Tree Modelling in Forest. Sensors, 8(6), 3938-3951. https://doi.org/10.3390/s8063938

Altuntas, C. (2023). Review of Scanning and Pixel Array-Based LiDAR Point-Cloud Measurement Techniques to Capture 3D Shape or Motion. Applied Sciences, 13(11), 6488. https://doi.org/10.3390/app13116488

Ortega, A., Silva, M., Teniente, E., Ferreira, R., Bernardino, A., Gaspar, J., & Andrade-Cetto, J. (2014). Calibration of an Outdoor Distributed Camera Network with a 3D Point Cloud. Sensors, 14(8), 13708-13729. https://doi.org/10.3390/s140813708

Deng, W., Chen, X., & Jiang, J. (2024). A Staged Real-Time Ground Segmentation Algorithm of 3D LiDAR Point Cloud. Electronics, 13(5), 841. https://doi.org/10.3390/electronics13050841

CANDAN, L., & KAÇAR, E. (2023). Methodology of real-time 3D point cloud mapping with UAV lidar. International Journal of Engineering and Geosciences, 8(3), 301-309. https://doi.org/10.26833/ijeg.1178260