Andreas Asmann
Papers
1
Total Citations
2
H-Index
1
About
Andreas Asmann is a researcher focused on computational imaging and efficient signal processing, with a particular emphasis on compressive sensing and depth reconstruction. His major contribution lies in developing mixed-precision solvers that enable rapid, low-power reconstruction of depth images from sparsely sampled LiDAR data—a critical capability for real-time applications in robotics, autonomous vehicles, and embedded systems. His 2021 ADMM case study on mixed-precision ℓ1 solvers demonstrates how algorithmic optimizations can reduce computational and memory demands without sacrificing reconstruction accuracy, making advanced sensing feasible on resource-constrained platforms like FPGAs. While his work has garnered early citations, its practical impact is underscored by its direct relevance to eye-safe, low-power LiDAR implementations. Asmann’s research bridges the gap between theoretical compressed sensing and deployable hardware solutions, offering a pathway to efficient, real-time 3D perception in autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1