Silvan Weder
Papers
1
Total Citations
2
H-Index
1
About
Silvan Weder is a researcher advancing the frontiers of 3D computer vision, with a primary focus on real-time semantic reconstruction and spatial understanding. His most notable contribution is the development of **ALSTER (A Local Spatio-Temporal Expert)**, a pioneering method for online 3D semantic segmentation from streaming RGB-D data. Unlike traditional offline approaches, ALSTER incrementally builds a dense 3D semantic map on-the-fly, making it directly applicable to robotics and mixed reality applications where real-time constraints are critical. This work addresses the fundamental challenge of balancing accuracy with computational efficiency in dynamic environments. While his research is still emerging, with ALSTER already garnering citations, Weder’s work is positioned at the intersection of geometric deep learning and embodied AI. His contributions promise to enable more intelligent autonomous systems that can understand and interact with their surroundings in real time, marking him as a rising innovator in the field of online 3D scene understanding.
Research Focus
Key Achievements
Top Papers
- 1