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arXiv · 2206.13500

Neural Neural Textures Make Sim2Real Consistent

Ryan Burgert·Jinghuan Shang·Xiang Li·Michael Ryoo
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Abstract

Unpaired image translation algorithms can be used for sim2real tasks, but many fail to generate temporally consistent results. We present a new approach that combines differentiable rendering with image translation to achieve temporal consistency over indefinite timescales, using surface consistency losses and \emph{neural neural textures}. We call this algorithm TRITON (Texture Recovering Image Translation Network): an unsupervised, end-to-end, stateless sim2real algorithm that leverages the underlying 3D geometry of input scenes by generating realistic-looking learnable neural textures. By settling on a particular texture for the objects in a scene, we ensure consistency between frames statelessly. Unlike previous algorithms, TRITON is not limited to camera movements -- it can handle the movement of objects as well, making it useful for downstream tasks such as robotic manipulation.

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Ryan Burgert, Jinghuan Shang, Xiang Li, Michael Ryoo. 2022-06-27. Neural Neural Textures Make Sim2Real Consistent. https://arxiv.org/abs/2206.13500

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