Published in arXiv pre-print, 2023
We propose Scene Flow via Distillation, a simple distillation framework that uses a label-free optimization method to produce pseudo-labels to supervise a feed forward model. Our technique yields state of the art results for endpoint error while still running 100x faster than the previously best performing methods.
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Published in arXiv pre-print, 2023
We propose a method to perform end-to-end learning on raw radar analog-to-digital (ADC) data from imaging radars. Specifically, we design a learnable signal processing module inside the neural network, and a pre-training method guided by traditional signal processing algorithms. The combination of these techniques allows us to achieve state of the art results on the RADIal raw radar dataset.
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Published in International Confderence on Intelligent Robots and Systems (IROS), 2021
A framework for calculating indirect estimations of state given observations of the behavior of other agents in the environment. We model traffic light estimation as such a problem and apply our framework to recover light state with over 70% accuracy using only the motion of other vehicles in the scene.
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