Google Research

Neural Voxel Renderer: Learning an Accurate and Controllable Rendering Tool

CVPR (2020) (to appear)

Abstract

We present a neural rendering framework that maps a voxelized scene into a high quality image. Highly-textured objects and scene element interactions are realistically rendered by our method, despite having a rough representation as an input. Moreover, our approach allows controllable rendering: geometric and appearance modifications in the input are accurately propagated to the output. The user can move, rotate and scale an object, change its appearance and texture or modify a light’s position and all these edits are represented in the final rendering. We demonstrate the effectiveness of our approach by rendering scenes with varying appearance, from single color per object to complex, high-frequency textures. We show that our re-rendering network can generate very precise and detailed images that capture the appearance of the input scene. Our experiments also illustrate that our approach achieves more accurate image synthesis results compared to alternatives and can also handle low voxel grid resolutions. Finally, we show how our neural rendering framework can be realistically applied to real scenes with diverse set of objects.

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