Selim Engin

• I'm a PhD student in Computer Science at UMN working with Prof. Volkan Isler in the Robotic Sensor Networks Lab.
• My main research interests lie in the general area of robotics, computer vision and machine learning applications. Particularly, I am interested in multi-agent systems, sequential decision making, 3d geometry in computer vision and generative models.
• Before coming to Minnesota, I did my undergraduate on mechatronics engineering in Istanbul. Between Jan-Aug 2019 I interned at Samsung AI Center NY.

Publications

Continuous Object Representation Networks: Novel View Synthesis without Target View Supervision
N. Hani, S. Engin, J.J. Chao, V. Isler
NeurIPS 2020
[pdf] [webpage] [code] [video]

Active Localization of Multiple Targets from Noisy Relative Measurements
S. Engin, V. Isler
WAFR 2020
[pdf] [webpage] [code] [video]

Higher Order Function Networks for View Planning and Multi-View Reconstruction
S. Engin, E. Mitchell, D. Lee, V. Isler, D.D. Lee
ICRA 2020
[pdf] [video]

Higher-Order Function Networks for Learning Composable 3D Object Representations
E. Mitchell, S. Engin, V. Isler, D.D. Lee
ICLR 2020
[pdf] [webpage]

Asynchronous Network Formation in Unknown Unbounded Environments
S. Engin, V. Isler
ICRA 2019
[pdf] [link] [video]

Minimizing Movement to Establish the Connectivity of Randomly Deployed Robots
S. Engin, V. Isler
ICAPS 2018
[pdf] [link]

Tracking Wildlife with Multiple UAVs: System Design, Safety and Field Experiments
H. Bayram, N. Stefas, S. Engin, V. Isler
MRS 2017
[pdf] [link]

Research Highlights

• How to generate novel views of objects using a small dataset as prior?
  Our method learns a continous object representation in 3D that allows us rendering images at desired viewpoints with the help of cyclic consistency losses.





• How should a manipulator arm move to visually inspect and reconstruct an object?
  We present a method that iteratively refines the reconstruction and uses it to plan for the next view for the camera.





• Suppose a group of disconnected robots are deployed in a large open area. What strategy should a robot pursue to disseminate a piece of information to the rest of the robots (whose positions are unknown!) as quickly as possible?