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
arXiv paper abstract https://arxiv.org/abs/2206.07435v1
arXiv PDF paper https://arxiv.org/pdf/2206.07435v1.pdf

… 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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I apply innovative technologies like machine learning, computer vision, and physics to further an organization's goals. Am recognized innovator with 66 patents.