Papers
1
Total Citations
24
H-Index
1
About
Runze Guo is a researcher advancing the field of computer vision, with a primary focus on RGBD semantic segmentation and multimodal feature fusion. His most cited work, the 2022 paper "Link-RGBD: Cross-Guided Feature Fusion Network for RGBD Semantic Segmentation" (24 citations), introduces an innovative module that addresses a critical challenge in deep learning: effectively extracting and integrating depth information to enhance semantic segmentation accuracy. By designing a cross-guided feature fusion mechanism, Guo’s approach enables richer, more complementary interactions between RGB and depth data, improving performance in complex visual scenes. This contribution has garnered attention for its practical impact on autonomous systems and scene understanding, where precise segmentation is vital. Guo’s research exemplifies a commitment to solving real-world vision problems through novel network architectures, and his work continues to influence subsequent studies in multimodal perception. With a growing citation record, he is establishing himself as a promising voice in the intersection of deep learning and 3D vision.
Research Focus
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Top Papers
- 1