Papers
19
Total Citations
618
H-Index
9
About
Cheng Wang is a prominent researcher specializing in 3D computer vision, point cloud processing, and autonomous systems, with particular expertise in LiDAR-based perception and deep learning for spatial data understanding. His most influential contribution, "Deep Learning on 3D Point Clouds" (2020), has amassed an impressive 365 citations, establishing itself as a foundational reference in the field and reflecting Wang's ability to synthesize complex technical landscapes for the broader research community. Wang has made significant strides in point cloud completion, authoring both a generative adversarial network-based dense completion method and a comprehensive 2024 survey that together address one of the field's core challenges — reconstructing complete 3D structures from partial data — with applications spanning autonomous driving, robotics, and medical imaging. His work on LiDAR-based localization, cooperative indoor mapping, and the innovative HSC4D framework — which captures dynamic human-scene interactions using wearable IMUs and LiDAR — demonstrates a consistent drive to bridge theoretical advances with real-world deployment. Additional contributions to FPGA-accelerated SLAM and cross-domain feature descriptors further illustrate his versatility. Collectively, Wang's research positions him as a key figure shaping the future of intelligent 3D scene understanding.
Research Focus
Key Achievements
Top Papers
- 1Review: Deep Learning on 3D Point Clouds365 citations · 2020
- 2Dense Point Cloud Completion Based on Generative Adversarial Network43 citations · 2021
- 3A Survey of Point Cloud Completion32 citations · 2024
- 4Cooperative indoor 3D mapping and modeling using LiDAR data28 citations · 2021
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- 9STCLoc: Deep LiDAR Localization With Spatio-Temporal Constraints14 citations · 2022
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