Papers
1
Total Citations
2
H-Index
1
About
Wen Yan is a researcher at the forefront of computer vision and 3D perception, with a primary focus on deep learning for point cloud analysis and object detection. His most-cited work, a comprehensive 2022 review on deep learning-based 3D object detection in indoor environments, addresses a critical gap in the literature—while most surveys emphasize outdoor autonomous driving scenarios, Yan’s review systematically synthesizes indoor-specific challenges and methodologies. This contribution has garnered early citations, reflecting its timely relevance for robotics, augmented reality, and smart building applications. Yan’s research advances the understanding of how neural networks can interpret complex 3D spatial data in cluttered, confined spaces, where occlusion and scale variation pose unique difficulties. By bridging the gap between outdoor-centric benchmarks and indoor deployment needs, his work provides a foundational resource for researchers developing perception systems for service robots, warehouse automation, and indoor navigation. Yan’s efforts highlight his commitment to making 3D vision more robust and applicable to real-world indoor environments, positioning him as an emerging voice in the field of 3D deep learning.
Research Focus
Key Achievements
Top Papers
- 1Deep Learning based 3D Object Detection in Indoor Environments: A Review2 citations · 2022