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Predict future depth and motion using only self-supervised raw images as input

Predict future depth and motion using only self-supervised raw images as input


Forecasting of depth and ego-motion with transformers and self-supervision



... paper addresses the problem of end-to-end self-supervised forecasting of depth and ego motion.


Given a sequence of raw images, the aim is to forecast both the geometry and ego-motion using a self supervised photometric loss.


The architecture is designed using both convolution and transformer modules.


... leverages the benefits of both modules: Inductive bias of CNN, and the multi-head attention of transformers, thus enabling a rich spatio-temporal representation that enables accurate depth forecasting.


... paper forecasts depth and ego motion using only self-supervised raw images as input.


... performs significantly well on the KITTI dataset benchmark with several performance criteria being even comparable to prior non-forecasting self-supervised monocular depth inference methods



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