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]
    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?