Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines

TL;DR

This paper introduces a practical deep learning method for view synthesis that efficiently generates high-quality novel views from sparse, irregularly sampled scene views, guided by a new sampling bound, enabling real-time virtual exploration.

Contribution

It extends plenoptic sampling theory to provide a sampling bound for view synthesis, allowing high-quality rendering with significantly fewer views than traditional methods.

Findings

Achieves Nyquist-rate perceptual quality with up to 4000x fewer views

Provides a practical view sampling bound for real-world scenes

Enables real-time virtual exploration on mobile and desktop platforms

Abstract

We present a practical and robust deep learning solution for capturing and rendering novel views of complex real world scenes for virtual exploration. Previous approaches either require intractably dense view sampling or provide little to no guidance for how users should sample views of a scene to reliably render high-quality novel views. Instead, we propose an algorithm for view synthesis from an irregular grid of sampled views that first expands each sampled view into a local light field via a multiplane image (MPI) scene representation, then renders novel views by blending adjacent local light fields. We extend traditional plenoptic sampling theory to derive a bound that specifies precisely how densely users should sample views of a given scene when using our algorithm. In practice, we apply this bound to capture and render views of real world scenes that achieve the perceptual quality of Nyquist rate view sampling while using up to 4000x fewer views. We demonstrate our approach's practicality with an augmented reality smartphone app that guides users to capture input images of a scene and viewers that enable realtime virtual exploration on desktop and mobile platforms.