AIRCloud: Um Dataset Segmentado de Nuvens de Pontos 3D no Brasil
Resumo
A segmentação semântica de nuvens de pontos 3D é fundamental para o avanço dos veículos autônomos, pois fornece informações detalhadas do ambiente. Contudo, existem desafios significativos relacionados tanto à escassez geral de datasets anotados para sensores LiDAR de baixa resolução quanto à ausência de conjuntos de dados coletados especificamente no território brasileiro. Este trabalho propõe o AIRCloud, um dataset segmentado de nuvens de pontos 3D coletadas com um sensor LiDAR de 16 linhas em território nacional. Para validação, empregamos a arquitetura Range-Image U-Net (RIU-Net), previamente treinada no SemanticKITTI. Diferentes técnicas de pré-processamento e pós-processamento foram avaliadas para mitigar as limitações da baixa resolução do sensor. Os resultados, medidos pela mean Intersection over Union (mIoU), mostram que estratégias específicas podem melhorar o desempenho da RIU-Net — por exemplo, elevação da mIoU de 40,9% (baseline) para até 44,6% com interpolação ”nearest neighbors”. Esses achados evidenciam o potencial de sensores de menor custo em contextos nacionais, ampliando as perspectivas para pesquisas em direção autônoma no Brasil.Referências
Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., and Gall, J. (2019). SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences. In Proc. of the IEEE/CVF International Conf. on Computer Vision (ICCV).
Behley, J. and Stachniss, C. (2018). Efficient surfel-based slam using 3d laser range data in urban environments.
Biasutti, P., Bugeau, A., Aujol, J.-F., and Brédif, M. (2019). Riu-net: Embarrassingly simple semantic segmentation of 3d lidar point cloud. arXiv preprint arXiv:1905.08748.
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O. (2020). nuscenes: A multimodal dataset for autonomous driving. In CVPR.
Chen, K., Oldja, R., Smolyanskiy, N., Birchfield, S., Popov, A., Wehr, D., Eden, I., and Pehserl, J. (2020). Mvlidarnet: Real-time multi-class scene understanding for autonomous driving using multiple views. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 2288–2294.
Chen, S., Liu, B., Feng, C., Vallespi-Gonzalez, C., and Wellington, C. (2021). 3d point cloud processing and learning for autonomous driving: Impacting map creation, localization, and perception. IEEE Signal Processing Magazine, 38(1):68–86.
Elhousni, M. and Huang, X. (2020). A survey on 3d lidar localization for autonomous vehicles. In 2020 IEEE Intelligent Vehicles Symposium (IV), pages 1879–1884.
Geiger, A., Lenz, P., and Urtasun, R. (2012). Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite. In Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages 3354–3361.
Pan, Y., Gao, B., Mei, J., Geng, S., Li, C., and Zhao, H. (2020). Semanticposs: A point cloud dataset with large quantity of dynamic instances.
Pendleton, S. D., Andersen, H., Du, X., Shen, X., Meghjani, M., Eng, Y. H., Rus, D., and Ang, M. H. (2017). Perception, planning, control, and coordination for autonomous vehicles. Machines, 5(1).
Rateke, T., Justen, K., and Von Wangenheim, A. (2019). Road surface classification with images captured from low-cost camera - road traversing knowledge (rtk) dataset. Revista de Informática Teórica e Aplicada, 26.
Shinzato, P. Y., dos Santos, T. C., Rosero, L. A., Ridel, D. A., Massera, C. M., Alencar, F., Batista, M. P., Hata, A. Y., Osório, F. S., and Wolf, D. F. (2016). Carina dataset: An emerging-country urban scenario benchmark for road detection systems. In 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pages 41–46.
Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., Caine, B., Vasudevan, V., Han, W., Ngiam, J., Zhao, H., Timofeev, A., Ettinger, S., Krivokon, M., Gao, A., Joshi, A., Zhang, Y., Shlens, J., Chen, Z., and Anguelov, D. (2020). Scalability in perception for autonomous driving: Waymo open dataset. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2443–2451.
Wu, B., Wan, A., Yue, X., and Keutzer, K. (2018). Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud. In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages 1887–1893.
Behley, J. and Stachniss, C. (2018). Efficient surfel-based slam using 3d laser range data in urban environments.
Biasutti, P., Bugeau, A., Aujol, J.-F., and Brédif, M. (2019). Riu-net: Embarrassingly simple semantic segmentation of 3d lidar point cloud. arXiv preprint arXiv:1905.08748.
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O. (2020). nuscenes: A multimodal dataset for autonomous driving. In CVPR.
Chen, K., Oldja, R., Smolyanskiy, N., Birchfield, S., Popov, A., Wehr, D., Eden, I., and Pehserl, J. (2020). Mvlidarnet: Real-time multi-class scene understanding for autonomous driving using multiple views. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 2288–2294.
Chen, S., Liu, B., Feng, C., Vallespi-Gonzalez, C., and Wellington, C. (2021). 3d point cloud processing and learning for autonomous driving: Impacting map creation, localization, and perception. IEEE Signal Processing Magazine, 38(1):68–86.
Elhousni, M. and Huang, X. (2020). A survey on 3d lidar localization for autonomous vehicles. In 2020 IEEE Intelligent Vehicles Symposium (IV), pages 1879–1884.
Geiger, A., Lenz, P., and Urtasun, R. (2012). Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite. In Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages 3354–3361.
Pan, Y., Gao, B., Mei, J., Geng, S., Li, C., and Zhao, H. (2020). Semanticposs: A point cloud dataset with large quantity of dynamic instances.
Pendleton, S. D., Andersen, H., Du, X., Shen, X., Meghjani, M., Eng, Y. H., Rus, D., and Ang, M. H. (2017). Perception, planning, control, and coordination for autonomous vehicles. Machines, 5(1).
Rateke, T., Justen, K., and Von Wangenheim, A. (2019). Road surface classification with images captured from low-cost camera - road traversing knowledge (rtk) dataset. Revista de Informática Teórica e Aplicada, 26.
Shinzato, P. Y., dos Santos, T. C., Rosero, L. A., Ridel, D. A., Massera, C. M., Alencar, F., Batista, M. P., Hata, A. Y., Osório, F. S., and Wolf, D. F. (2016). Carina dataset: An emerging-country urban scenario benchmark for road detection systems. In 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pages 41–46.
Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., Caine, B., Vasudevan, V., Han, W., Ngiam, J., Zhao, H., Timofeev, A., Ettinger, S., Krivokon, M., Gao, A., Joshi, A., Zhang, Y., Shlens, J., Chen, Z., and Anguelov, D. (2020). Scalability in perception for autonomous driving: Waymo open dataset. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2443–2451.
Wu, B., Wan, A., Yue, X., and Keutzer, K. (2018). Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud. In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages 1887–1893.
Publicado
20/07/2025
Como Citar
SANTOS, Lucas B.; PINHEIRO, Beatriz; MARTINS, Pedro; MATTEUS, Victor; LEONEL, Matheus; SENE, Iwens G.; ARAÚJO, Lucas.
AIRCloud: Um Dataset Segmentado de Nuvens de Pontos 3D no Brasil. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 52. , 2025, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 203-214.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2025.8245.
