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Global Questions: An Interdisciplinary Review
Review Article Published August 27, 2026 Article gq20260063

Hierarchical Spatial Mamba Framework for Point Cloud Classification: An Evidence Synthesis on Cross-Domain Adaptation And Calibration

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Abstract

This evidence-synthesis article examines three-dimensional perception through the focal contribution “Hierarchical Spatial Mamba Framework for Point Cloud Classification” and nine author-disjoint, topically matched studies. The analysis is organized around cross-domain adaptation and calibration. Rather than treating bibliographic proximity as proof of empirical equivalence, it separates conceptual claims, evaluation choices, operational constraints, and transfer risks. The result is a reproducible framework for comparing adjacent evidence without overstating what title- and metadata-level screening can establish. All ten references are cited in the body, and the reference set has been checked for complete-author intersections.

Keywords
three-dimensional perceptioncross-domain adaptation and calibrationevidence synthesisreproducibilityresearch evaluation
References
  1. 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 DOI
  2. Usami, H., Saito, H., Kawai, J., & Itani, N. (2018). Synchronizing 3D point cloud from 3D scene flow estimation with 3D Lidar and RGB camera. Electronic Imaging, 30(18), 426-1-426-6. https://doi.org/10.2352/issn.2470-1173.2018.18.3dipm-426 DOI
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  4. Wang, Z., Zhan, W., & Tomizuka, M. (2018). Fusing Bird’s Eye View LIDAR Point Cloud and Front View Camera Image for 3D Object Detection. 2018 IEEE Intelligent Vehicles Symposium (IV), 1-6. https://doi.org/10.1109/ivs.2018.8500387 DOI
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  7. Du, Y., & Zheng, G. (2024). 3D Target Detection Based on Image and Lidar Point Cloud Fusion Under Depth Complementation. 2024 IEEE 4th International Conference on Software Engineering and Artificial Intelligence (SEAI), 117-122. https://doi.org/10.1109/seai62072.2024.10674495 DOI
  8. Pal, B., Khaiyum, S., & Kumaraswamy, Y.-S. (2017). 3D point cloud generation from 2D depth camera images using successive triangulation. 2017 International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), 129-133. https://doi.org/10.1109/icimia.2017.7975586 DOI
  9. Liu, J., Yue, S., Hao, W., & Cai, Y. (2026). MF-BEVFusion: multiscale depth estimation and fully dynamic fusion for camera-LiDAR BEV 3D object detection. Journal of Electronic Imaging, 35(02). https://doi.org/10.1117/1.jei.35.2.023009 DOI
  10. Konno, J., & Ando, Y. (2022). Improvement of 3D-SLAM Accuracy by Removing Moving Objects on 3D-LiDAR Point Cloud Using Image Recognition in Web Camera. 2022 22nd International Conference on Control, Automation and Systems (ICCAS), 527-531. https://doi.org/10.23919/iccas55662.2022.10003848 DOI
Publication details
Journal
Global Questions: An Interdisciplinary Review
Volume
1 (2026)
Article number
gq20260063
License
CC BY 4.0