Javier Calpe
Papers
1
Total Citations
4
H-Index
1
About
Javier Calpe is a leading researcher in computer vision and sensor fusion, with a primary focus on depth estimation and Time-of-Flight (ToF) imaging systems. His most-cited work, "Guided Depth Inpainting in ToF Image Sensing Based on Near Infrared Information" (2025, 4 citations), addresses a critical challenge in robotics, augmented reality, and autonomous driving: the accurate reconstruction of missing depth values caused by invalid pixels and low-light conditions. Calpe’s key contribution lies in developing a guided inpainting method that leverages near-infrared (NIR) information to fill depth gaps, significantly enhancing the robustness of ToF sensors in real-world environments. This work has already garnered attention for its practical impact on improving depth accuracy in dynamic scenes. Beyond this, Calpe’s research spans multi-modal sensor integration, where he explores the synergy between RGB, NIR, and depth data to boost perception reliability. His achievements include advancing the state-of-the-art in depth completion, with implications for safer autonomous navigation and more immersive AR experiences. As a rising figure in the field, Calpe’s innovative approaches continue to shape how machines perceive and interact with three-dimensional spaces.
Research Focus
Key Achievements
Top Papers
- 1