Multi-Perspective Stereoscopy from Light Fields

晃晃

Multi-Perspective Stereoscopy from Light Fields

Changil Kim  Alexander Hornung  Simon Heinzle  Wojciech Matusik  Markus Gross

ETH Zurich  Disney Research Zurich   MIT CSAIL







Abstract

This paper addresses stereoscopic view generation from a light

field. We present a framework that allows for the generation

of stereoscopic image pairs with per-pixel control over disparity,

based on multi-perspective imaging from light fields. The proposed

framework is novel and useful for stereoscopic image processing

and post-production. The stereoscopic images are computed as

piecewise continuous cuts through a light field, minimizing an en-

ergy reflecting prescribed parameters such as depth budget, maxi-

mum disparity gradient, desired stereoscopic baseline, and so on.

As demonstrated in our results, this technique can be used for ef-

ficient and flexible stereoscopic post-processing, such as reducing

excessive disparity while preserving perceived depth, or retargeting

of already captured scenes to various view settings. Moreover, we

generalize our method to multiple cuts, which is highly useful for

content creation in the context of multi-view autostereoscopic dis-

plays. We present several results on computer-generated content as

well as live-action content.

Keywords: stereoscopy, light field, multi-perspective imaging, au-

tostereoscopic display, post-production

1 Introduction

Three-dimensional stereoscopic television, movies, and games have

been gaining more and more popularity both within the entertain-

ment industry and among consumers. An ever increasing amount

of content is being created, distribution channels including live-

broadcast are being developed, and stereoscopic monitors and TV

sets are being sold in all major electronic stores. With novel genera-

tions of autostereoscopic and multi-view autostereoscopic displays

even glasses-free solutions become available to the consumer.

However, the task of creating convincing yet perceptually pleasing

stereoscopic content remains difficult. This is mainly because post-

processing tools for stereo are still underdeveloped, and one often

has to resort to traditional monoscopic tools and workflows, which

are generally ill-suited for stereo-specific issues [Mendiburu 2009].

This situation creates an opportunity to rethink the whole post-

processing pipeline for stereoscopic content creation and editing. In

the past the computer graphics community has greatly contributed

to the development of novel tools for image and video processing.

One particular example in the context of this work is the recent

progress on light field capture and processing, which enables post-

acquisition content modification such as depth-of-field, focus, or

viewpoint changes. A variety of prototypes for light field acqui-

sition have been developed [Adelson and Wang 1992; Yang et al.

2002; Ng et al. 2005; Wilburn et al. 2005; Georgiev et al. 2006;

Veeraraghavan et al. 2007] such that we can expect plenoptic cam-

eras to become available in the near future. However, the concept

of post-acquisition control and editing is missing in stereoscopic

post-processing.

The main cue responsible for stereoscopic scene perception is

binocular parallax (or binocular disparity) and therefore tools for

its manipulation are extremely important. One of the most common

methods for controlling the amount of binocular parallax is based

on setting the baseline, or the inter-axial distance, of two cameras

prior to acquisition. However, the range of admissible baselines is

quite limited since most scenes exhibit more disparity than humans

can tolerate when viewing the content on a stereoscopic display.

Reducing baseline decreases the amount of binocular disparity;

but it also causes scene elements to be overly flat. The second,

more sophisticated approach to disparity control requires remap-

ping image disparities (or remapping the depth of scene elements),

and then re-synthesizing new images. This approach has consider-

able disadvantages as well; for content captured with stereoscopic

camera rigs, it typically requires accurate disparity computation

and hole filling of scene elements that become visible in the re-

synthesized views. For computer-generated images, changing the

depth of the underlying scene elements is generally not an option,

because changing the 3D geometry compromises the scene compo-

sition, lighting calculations, visual effects, etc [Neuman 2010].













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