TL;DR
This paper introduces a neural rendering approach that decomposes dynamic scenes into scene graphs, enabling realistic view synthesis and object manipulation from video data, extending static scene methods to dynamic, object-based representations.
Contribution
It presents the first neural rendering method that models dynamic scenes with scene graphs, encoding object transformations and radiance for flexible scene manipulation.
Findings
Successfully models dynamic scenes from video data
Enables rendering of novel views with unseen objects and poses
Validates on synthetic and real automotive datasets
Abstract
Recent implicit neural rendering methods have demonstrated that it is possible to learn accurate view synthesis for complex scenes by predicting their volumetric density and color supervised solely by a set of RGB images. However, existing methods are restricted to learning efficient representations of static scenes that encode all scene objects into a single neural network, and lack the ability to represent dynamic scenes and decompositions into individual scene objects. In this work, we present the first neural rendering method 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…
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