OSCAM - Optimized Stereoscopic Camera Control for Interactive 3D

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OSCAM - Optimized Stereoscopic Camera Control for Interactive 3D

Thomas Oskam1;2 Alexander Hornung 2 Huw Bowles 3;4 Kenny Mitchell 3;4 Markus Gross 1;2

1 ETH Zurich 2 Disney Research Zurich 3 Black Rock Studio 4 Disney Interactive Studios







Abstract

This paper presents a controller for camera convergence and inter-

axial separation that specifically addresses challenges in interactive

stereoscopic applications like games. In such applications, unpre-

dictable viewer- or object-motion often compromises stereopsis due

to excessive binocular disparities. We derive constraints on the

camera separation and convergence that enable our controller to

automatically adapt to any given viewing situation and 3D scene,

providing an exact mapping of the virtual content into a comfort-

able depth range around the display. Moreover, we introduce an

interpolation function that linearizes the transformation of stereo-

scopic depth over time, minimizing nonlinear visual distortions.

We describe how to implement the complete control mechanism

on the GPU to achieve running times below 0.2ms for full HD.

This provides a practical solution even for demanding real-time

applications. Results of a user study show a significant increase

of stereoscopic comfort, without compromising perceived realism.

Our controller enables ‘fail-safe’ stereopsis, provides intuitive con-

trol to accommodate to personal preferences, and allows to properly

display stereoscopic content on differently sized output devices.

CR Categories: I.3.3 [Computer Graphics]: Picture/Image

generation—display algorithms,viewing algorithms;

Keywords: stereoscopic 3D, disparity control, real-time graphics,

games, interactive 3D



1 Introduction

Stereoscopic content creation, processing, and display has become

a pivotal element in movies and entertainment, yet the industry is

still confronted with various difficult challenges. Recent research

has made substantial progress in some of these areas [Lang et al.

2010; Koppal et al. 2011; Didyk et al. 2011; Heinzle et al. 2011].

Most of these works focus on the classical production pipeline,

where the consumer views ready-made content that has been op-

timized in (post-) production to ensure a comfortable stereoscopic

experience. See Tekalp et al. [2011] for an overview.

In interactive applications that create stereoscopic output in real-

time, one faces a number of fundamentally different challenges

[Gateau and Neuman 2010]. For example, in a first-person game

where the player is in control of the view, a simple collision with a

wall or another object will result in excessive disparities that cause

visual fatigue or destroy stereopsis (see Figure 1). In order to guar-

antee proper stereoscopy, one needs a controller that adjusts the

range of disparities to the viewer’s preferences. An example for

such a controller is the work of Lang et al. [2010] which, how-

ever, has been designed for post-capture disparity range adaptation

using complex image-domain warping techniques. In a game en-

vironment where the stereoscopic output is created and displayed

in real-time, it is advisable to optimize the stereoscopic rendering

parameters, i.e., camera convergence and interaxial separation, and

to avoid computationally expensive solutions.

The problem can be formulated as one of controlling perceived

depth. We use the term ‘perceived depth’ in the geometrical sense,

where the distances reconstructed by the viewer are dominated by

the observed screen disparities. Even though there are other im-

portant cues such as vertical size, focus that influence perceived

depth [Backus et al. 1999; Watt et al. 2005], the work of Held and

Banks [2008] showed that the geometrical approach is a valid ap-

proximation. The range of perceived depth around the screen that

can be viewed comfortably is generally referred to as the comfort

zone, and is defined as the range of positive and negative disparities

that can be comfortably watched by each individual viewer [Smolic

et al. 2011; Shibata et al. 2011]. Therefore, we are looking for

an exact mapping of a specific range of distances in the scene into

this depth volume around the screen. In the course of this article,

we will refer to this volume as the target depth range. While we

concentrate on the control of the mapping between the virtual and

real space there exists prior work on how to derive a comfortable

target depth range [Woods et al. 1993; Shibata et al. 2011]









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