Yehonathan Litman

I'm a third year PhD student at in the Robotics Institute, Carnegie Mellon University, advised by Shubham Tulsiani and Fernando De la Torre.

I'm focusing on how large generative models can serve as priors for faithfully reconstructing the world and capturing its underlying intrinsic properties. My work is supported in part by an NSF Graduate Research Fellowship.

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Selected Research

Distill a material diffusion prior model into material parameters during inverse rendering to improve intrinsic decomposition and novel relighting.

Reconstruct input images in a 3D occupancy grid and project to bird's eye view of the environment, register that to satelite imagery data to correct SLAM drift in real time without requiring GPS.

Dynamically assign a drone agent leader to heuristically assemble a large variety of drone swarm formations using only vision and onboard sensors.

Retain features across frames selectively based on shared information and use hardware acceleration to dramatically improve VIO SLAM efficiency.

Addressing the visual observability problem in VIO by using invariant filtering to improve efficiency and accuracy.

Awards

Misc.

Credits: Jon Barron.