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• W2912182345 abstract "Here we introduce an approximated differentiable renderer to refine a 6-DoF pose prediction using only 2D alignment information. To this end, a two-branched convolutional encoder network is employed to jointly estimate the object class and its 6-DoF pose in the scene. We then propose a new formulation of an approximated differentiable renderer to re-project the 3D object on the image according to its predicted pose; in this way the alignment error between the observed and the re-projected object silhouette can be measured. Since the renderer is differentiable, it is possible to back-propagate through it to correct the estimated pose at test time in an online learning fashion. Eventually we show how to leverage the classification branch to profitably re-project a representative model of the predicted class (i.e. a medoid) instead. Each object in the scene is processed independently and novel viewpoints in which both objects arrangement and mutual pose are preserved can be rendered. Differentiable renderer code is available at: https://github.com/ndrplz/tensorflow-mesh-renderer ." @default.
• W2912182345 created "2019-02-21" @default.
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• W2912182345 date "2019-01-01" @default.
• W2912182345 modified "2023-09-24" @default.
• W2912182345 title "End-to-End 6-DoF Object Pose Estimation Through Differentiable Rasterization" @default.
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• W2912182345 doi "https://doi.org/10.1007/978-3-030-11015-4_53" @default.
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