Minh-Khoi Tran
Papers
1
Total Citations
346
H-Index
1
About
Minh-Khoi Tran is a leading researcher in computer vision and robotics, best known for his foundational work on 3D scene understanding and RGB-D datasets. His most influential contribution, the SceneNN dataset (2016, 346 citations), addressed a critical gap in the field by providing a richly annotated mesh-based dataset of indoor scenes. Unlike prior RGB-D datasets, SceneNN offered fine-grained, per-vertex semantic and instance labels, enabling more accurate training and evaluation of models for scene segmentation, object recognition, and spatial reasoning. This work has become a benchmark resource, driving advances in how machines perceive and interact with complex, real-world environments. Tran’s research has significantly impacted the development of intelligent systems capable of navigating and understanding cluttered spaces, with his dataset widely adopted by both academic and industrial labs. Through SceneNN and related projects, he has helped shape modern approaches to 3D vision, making his contributions essential reading for students and researchers working at the intersection of computer vision, robotics, and deep learning.
Research Focus
Key Achievements
Top Papers
- 1SceneNN: A Scene Meshes Dataset with aNNotations346 citations · 2016