Neural Scene Graphs for Dynamic Scenes (CVPR 2021)

We present a first neural rendering approach that decomposes dynamic scenes into scene graphs. We propose a learned scene graph representation, which encodes object transformation and radiance, to efficiently render novel arrangements and views of the scene.

To this end, we learn implicitly encoded scenes, combined with a jointly learned latent representation to describe objects with a single implicit function. We assess the proposed method on synthetic and real automotive data, validating that our approach learns dynamic scenes – only by observing a video of this scene – and allows for rendering novel photo-realistic views of novel scene compositions with unseen sets of objects at unseen poses.

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Publication:
The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021