Off-Road LiDAR Intensity Based Semantic Segmentation

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Biavati, G., Donfrancesco, G.D., Cairo, F., Feist, D.G.: Correction scheme for close-range lidar returns. Appl. Opt. 50(30), 5872–5882 (Oct 2011), https://opg.optica.org/ao/abstract.cfm?URI=ao-50-30-5872

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Thomas, H., Qi, C.R., Deschaud, J.E., Marcotegui, B., Goulette, F., Guibas, L.: Kpconv: Flexible and deformable convolution for point clouds. In: IEEE/CVF International Conference on Computer Vision. pp. 6410–6419 (2019)

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Cortinhal, T., Tzelepis, G., Erdal Aksoy, E.: Salsanext: Fast, uncertainty-aware semantic segmentation of lidar point clouds. In: Bebis, G., Yin, Z., Kim, E., Bender, J., Subr, K., Kwon, B.C., Zhao, J., Kalkofen, D., Baciu, G. (eds.) Advances in Visual Computing. pp. 207–222. Springer International Publishing, Cham (2020)

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Aksoy, E.E., Baci, S., Cavdar, S.: Salsanet: Fast road and vehicle segmentation in lidar point clouds for autonomous driving. In: IEEE Intelligent Vehicles Symposium (IV2020) (2020)

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Jiang, P., Osteen, P., Wigness, M., Saripalli, S.: Rellis-3d dataset: Data, benchmarks and analysis. In: IEEE International Conference on Robotics and Automation (ICRA). pp. 1110–1116 (2021)

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Yu, J., Chen, J., Dabbiru, L., Goodin, C.T.: Analysis of LiDAR configurations on off-road semantic segmentation performance. In: Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure. vol. 12540, p. 1254003. SPIE (2023), https://doi.org/10.1117/12.2663098

2023

Later among the works it cites.

Cheng, Y.T., Lin, Y.C., Habib, A.: Generalized lidar intensity normalization and its positive impact on geometric and learning-based lane marking detection. Remote Sensing 14(17) (2022), https://www.mdpi.com/2072-4292/14/17/4393

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Closest in time.