UnstructuredLumigraphRendering.PDF资源-CSDN文库

Unstructured

Rendering.PDF

需积分: 13 93 浏览量 2011-03-12 21:55:18 上传评论收藏 3.04MB PDF 举报

资源推荐

资源详情

资源评论

Unstructured Lumigraph Rendering

Chris Buehler Michael Bosse Leonard McMillan Steven Gortler Michael Cohen

MIT Laboratory for Computer Science Harvard University Microsoft Research

Abstract

We describe an image based rendering approach that generalizes

many current image based rendering algorithms, including light

ﬁeld rendering and view-dependent texture mapping. In particular,

it allows for lumigraph-style rendering from a set of input cameras

in arbitrary conﬁgurations (i.e., not restricted to a plane or to any

speciﬁc manifold). In the case of regular and planar input camera

positions, our algorithm reduces to a typical lumigraph approach.

When presented with fewer cameras and good approximate geom-

etry, our algorithm behaves like view-dependent texture mapping.

The algorithm achieves this ﬂexibility because it is designed to meet

a set of speciﬁc goals that we describe. We demonstrate this ﬂexi-

bility with a variety of examples.

Keyword Image-Based Rendering

1 Introduction

Image-based rendering (IBR) has become a popular alternative to

traditional three-dimensional graphics. Two effective IBR meth-

ods are view-dependent texture mapping (VDTM) [3] and the light

ﬁeld/lumigraph [10, 5] approaches. The light ﬁeld and VDTM algo-

rithms are in many ways quite different in their assumptions and in-

put. Light ﬁeld rendering requires a large collection of images from

cameras whose centers lie on a regularly sampled two-dimensional

patch, but it makes few if any assumptions about the geometry of

the scene. In contrast, VDTM assumes a relatively accurate ge-

ometric model, but requires only a small number of images from

input cameras that can be in general positions. These images are

then “projected” onto the geometry for rendering.

We suggest that, at their core, these two approaches are quite

similar. Both are methods for interpolating color values for a de-

sired ray as some combination of input rays. In VDTM this inter-

polation is performed using a geometric model to determine which

pixel from each input image “corresponds” to the desired ray in the

output image. Of these corresponding rays, those that are closest in

angle to the desired ray are weighted to make the greatest contribu-

tion to the interpolated result.

Light ﬁeld rendering can be similarly interpreted. For each de-

sired ray (s, t, u, v), one searches the image database for rays that

intersect near some (u, v) point on a “focal plane” and have a simi-

lar angle to the desired ray, as measured by the ray’s intersection on

the “camera plane” (s, t). In a depth-corrected lumigraph, the focal

plane is effectively replaced with an approximate geometric model,

making this approach even more similar to view dependent texture

mapping.

Given these related IBR approaches, we attempt to address the

following questions: Is there a generalized rendering framework

that spans all of these image-based rendering algorithms, having

VDTM and lumigraph/light ﬁelds as extremes? Might such an al-

gorithm adapt well to various numbers of input images from cam-

eras in general conﬁgurations while also permitting various levels

of geometric accuracy?

In this paper we approach the problem by suggesting a set of

goals that any image based rendering algorithm should have. We

ﬁnd that no previous IBR algorithm simultaneously satisﬁes all of

these goals. Therefore these algorithms behave quite well under

appropriate assumptions on their input, but may produce unneces-

sarily poor renderings when these assumptions are violated.

We then describe an algorithm for “unstructured lumigraph ren-

dering” (ULR), that generalizes both lumigraph and VDTM render-

ing. Our algorithm is designed speciﬁcally with the stated goals in

mind. As a result, our renderer behaves well with a wide variety

of inputs. These include source cameras that are not on a com-

mon plane, such as source images taken by moving forward into a

scene, a conﬁguration that would be problematic for previous IBR

approaches.

It should be no surprise that our algorithm bears many resem-

blances to earlier approaches. The main contribution of our algo-

rithm is that, unlike previously published methods, it is designed to

meet a set of listed goals. Thus, it works well on a wide range of

differing inputs, from few images with an accurate geometric model

to many images with minimal geometric information.

2 Previous Work

The basic approach to view dependent texture mapping (VDTM) is

put forth by Debevec et al. [3] in their Fac¸ade image-based model-

ing and rendering system. Fac¸ade is designed to estimate geometric

models consistent with a small set of source images. As part of this

system, a rendering algorithm was developed where pixels from all

relevant cameras were combined and weighted to determine a view-

dependent texture for the derived geometric models. In later work,

Debevec et al [4] describe a real-time VDTM algorithm. In this

algorithm, each polygon in the geometric model maintains a “view

map” data structure that is used to quickly determine a set of three

input cameras that should be used to texture it. Like most real-time

VDTM algorithms, this algorithm uses hardware supported projec-

tive texture mapping [6] for efﬁciency.

At the other extreme, Levoy and Hanrahan [10] describe the light

ﬁeld rendering algorithm, in which a large collection of images are

used to render novel views of a scene. This collection of images

is captured from cameras whose centers lie on a regularly sampled

two-dimensional plane. Light ﬁelds otherwise make few assump-

tions about the geometry of the scene. Gortler et al. [5] describe

a similar rendering algorithm called the lumigraph. In addition,

the authors of the lumigraph paper suggest many workarounds to

overcome limitations of the basic approach, including a “rebinning”

process to handle source images acquired from general camera po-

sitions and a “depth-correction” extension to allow for more ac-

curate ray reconstructions from an insufﬁcient number of source

cameras.

Many extensions, enhancements, alternatives, and variations

to these basic algorithms have since been suggested. These in-

clude techniques for rendering digitized three-dimensional models

in combination with acquired images such as Pulli et al. [13] and

Wood et al. [18]. Shum et al. [17] suggests alternate lower di-

mensional lumigraph approximations that use approximate depth

correction. Heigl et al. [7] describe an algorithm to perform IBR

from an unstructured set of data cameras where the projections of

the source cameras’ centers were projected into the desired im-

age plane, triangulated, and used to reconstruct the interior pixels.

Isaksen et al. [9] show how the common “image-space” coordinate

frames used in light ﬁeld rendering can be viewed as a focal plane

for dynamically generating alternative ray reconstructions. A for-

mal analysis of the trade off between the number of cameras and

the ﬁdelity of geometry is presented in [1].

3 Goals

We begin by presenting a list of desirable properties that we feel an

ideal image-based rendering algorithm should have. No previously

published method satisﬁes all of these goals. In the following sec-

tion we describe a new algorithm that attempts to meet these goals

while maintaining interactive rendering rates.

Use of geometric proxies: When geometric knowledge is

present, it should be used to assist in the reconstruction of a desired

ray (see Figure 1). We refer to such approximate geometric infor-

mation as a proxy. The combination of accurate geometric proxies

with nearly Lambertian surface properties allows for high quality

reconstructions from relatively few source images. The reconstruc-

tion process merely entails looking for rays from source cameras

that see the “same” point. This idea is central to all VDTM algo-

rithms. It is also the distinguishing factor in geometry-corrected lu-

migraphs and surface light ﬁeld algorithms. Approximate proxies,

such as the focal-plane abstraction used by Isaksen [9], allow for

the accurate reconstruction of rays at speciﬁc depths from standard

light ﬁelds.

With a highly accurate geometric model, the visibility of any

surface point relative to a particular source camera can also be de-

termined. If a camera’s view of the point is occluded by some other

point on the geometric model, then that camera should not be used

in the reconstruction of the desired ray. When possible, image-

based algorithms should consider visibility in their reconstruction.

Figure 1: When available, approximate geometric information

should be used to determine which source rays correspond well to

a desired ray. Here C

denotes the position of a reference camera,

and D is desired novel viewpoint.

Unstructured input: It is also desirable for an image-based

rendering algorithm to accept input images from cameras in gen-

eral position. The original light ﬁeld method assumes that the cam-

eras are arranged at evenly spaced positions on a single plane. This

limits the applicability of this method since it requires a special

capture gantry that is both expensive and difﬁcult to use in many

settings [11].

The lumigraph paper describes an acquisition system that uses a

hand-held video camera to acquire input images [5]. They apply a

preprocessing step, called rebinning, that resamples the input im-

ages from virtual source cameras situated on a regular grid. This

rebinning process adds an additional reconstruction and sampling

step to lumigraph creation. This extra step tends to degrade the

overall quality of the representation. This can be demonstrated by

noting that a rebinned lumigraph cannot, in general, reproduce its

input images. The surface light ﬁeld algorithm suffers from essen-

tially the same resampling problem.

Epipole consistency: When a desired ray passes through the

center of projection of a source camera it can be trivially re-

constructed from the ray database (assuming a sufﬁciently high-

resolution input image and the ray falls within the camera’s ﬁeld-

of-view) (see Figure 2). In this case, an ideal algorithm should

return a ray from the source image. An algorithm with epipole

consistency will reconstruct this ray correctly without any geomet-

ric information. With large numbers of source cameras, algorithms

with epipole consistency can create accurate reconstructions with

essentially no geometric information. Light ﬁeld and lumigraph al-

gorithms are designed speciﬁcally to maintain this property.

Surprisingly, many real-time VDTM algorithms do not ensure

this property, even approximately, and therefore, will not work

properly when given poor geometry. The algorithms described

in [13, 2] reconstruct all of the rays in a ﬁxed desired view using

a ﬁxed selection of three source images but, as shown by the origi-

nal light ﬁeld paper, proper reconstruction of a desired image may

involve using some rays from each of the source images. The algo-

rithm described in [4] always uses three source cameras to recon-

struct all of the desired pixels on a polygon of the geometry proxy.

This departs from epipole consistency if the proxy is coarse. The

algorithm of Heigl et al. [7] is an notable exception that, like a light

ﬁeld or lumigraph, maintains epipole consistency.

Figure 2: When a desired ray passes through a source camera cen-

ter, that source camera should be emphasized most in the recon-

struction. Here this case occurs for cameras C

, C

, and C

Minimal angular deviation: In general, the choice of which

input images are used to reconstruct a desired ray should be based

on a natural and consistent measure of closeness (See Figure 3). In

particular, source image rays with similar angles to the desired ray

should be used when possible.

剩余7页未读，继续阅读

评论收藏

内容反馈

lvzhigao2008

粉丝: 0
资源: 6

Unstructured Lumigraph Rendering.PDF

最新资源

Unstructured Lumigraph Rendering.PDF

COMSOLMultiphysics技术FAQ问答终版.pdf

基于深度学习的电子病历命名实体识别及其在知识发现中的应用.pdf

Pro-Deep-Learning-with-TensorFlow.pdf

藏经阁-阿里巴巴大数据产品最新特性介绍—DataWorks.pdf

Text Analytics with Python, 2nd Edition.pdf

cameraReady_enliangCVPR2014.pdf

智东西公开课-玩转模块化AI-让智能更简单-触景无限CEO肖洪波.pdf

2020届江苏省沭阳县高三下学期联考英语试题扫描版含听力.pdf

FIBOCOM-USSD功能介绍.pdf

《计算机专业英语》作业.pdf

1_CCKS_ATT_XifengYan.pdf

ManagingMultimediaandUnstructuredDataintheOracleDatabaseFreePdfBook.pdf 英文原版

Knowledge Aware Conversation Generation Reasoning onAugmentedGraphs.pdf

unstr2d (2d codes for unstructured CFD solver)

Python Web Scraping Cookbook-Packt Publishing(2018).pdf )

Research Advances in Cloud Computing-Springer(2017).pdf

VTK培训的教程.pdf

a survery of ontology learning approaches.pdf

HR专业英语.pdf

最新资源