SGANVO uses stacked GAN layers with recurrent connections to estimate depth and ego-motion unsupervisedly from images, reporting better or comparable results on the KITTI dataset.
”GANVO: Unsupervised Deep Monocular Visual Odometry and Depth Estimation with Generative Adversarial Networks.” arXiv preprint arXiv:1809.05786 (2018)
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2019 2verdicts
UNVERDICTED 2representative citing papers
Decomposing scene motion into per-point 6DoF motion maps from optical flow and depth enables a neural network to estimate camera motion more accurately than stacking raw inputs.
citing papers explorer
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SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks
SGANVO uses stacked GAN layers with recurrent connections to estimate depth and ego-motion unsupervisedly from images, reporting better or comparable results on the KITTI dataset.
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Scene Motion Decomposition for Learnable Visual Odometry
Decomposing scene motion into per-point 6DoF motion maps from optical flow and depth enables a neural network to estimate camera motion more accurately than stacking raw inputs.