Sibo Geng

Papers

Total Citations

110

H-Index

About

Sibo Geng is a researcher specializing in 3D LiDAR perception, semantic segmentation, and machine learning applications for autonomous systems. His work sits at the intersection of computer vision, robotics, and autonomous driving — fields that are rapidly reshaping modern transportation and intelligent systems. Geng's most significant contribution is his comprehensive survey and experimental study on 3D LiDAR semantic segmentation, which has accumulated over 99 citations since its 2021 publication. This work addresses a critical bottleneck in the field: the scarcity of fine-annotated 3D LiDAR datasets, which are notoriously labor-intensive and technically demanding to produce. By systematically reviewing existing datasets and deep learning methodologies, Geng and his collaborators provided the research community with an invaluable reference for understanding the current landscape and limitations of the field. His iterative approach — publishing and refining versions of this survey between 2020 and 2021 — demonstrates a rigorous commitment to thoroughness and scientific accuracy. For students and researchers entering autonomous perception research, Geng's work serves as an essential foundation, clearly mapping the challenges of data hunger in 3D semantic segmentation and guiding future directions for more scalable and efficient solutions.