Self-Improving Multiplane-to-Layer Images for Novel View Synthesis

Abstract

We present a new method for lightweight novel-view synthesis that generalizes to an arbitrary forward-facing scene. Recent approaches are computationally expensive, require per-scene optimization, or produce a memory-expensive representation. We start by representing the scene with a set of fronto-parallel semitransparent planes and afterwards convert them to deformable layers in an end-to-end manner. Additionally, we employ a feed-forward refinement procedure that corrects the estimated representation by aggregating information from input views. Our method does not require any fine-tuning when a new scene is processed and can handle an arbitrary number of views without any restrictions. Experimental results show that our approach surpasses recent models in terms of both common metrics and human evaluation, with the noticeable advantage in inference speed and compactness of the inferred layered geometry.