Interactive 3D Modeling

A Survey-based Perspective on Interactive 3D Reconstruction

Julius Sch¨oning and Gunther Heidemann

Institute of Cognitive Science, University of Osnabr¨uck, Osnabr¨uck, Germany

Keywords:

Image-based Modelling, 3D Reconstruction, Interactive, User Centered, CAD-ready, Survey.

Abstract:

3D reconstruction and modeling techniques based on computer vision show a signiﬁcant improvement in

recent decades. Despite the great variety, a majority of these techniques depend on speciﬁc photographic

collections or video footage. For example, most are designed for large data collections, overlapping photos,

captures from turntables or photos with lots of detectable features such as edges. If the input, however, does not

ﬁt the particular speciﬁcation, most techniques can no longer create reasonable 3D reconstructions. We review

the work in the research area of 3D reconstruction and 3D modeling with a focus on the speciﬁc capabilities of

these methods and possible drawbacks. Within this literature review, the practical usability with the focus on

the input data — the collections of photographs or videos — and on the resulting models are discussed. Upon

this basis, we introduce our position of interactive 3D reconstruction and modeling as a possible opportunity of

lifting current restrictions from these techniques, which leads to the possibility of creating CAD-ready models

in the future.

1 INTRODUCTION

A multitude of computer vision based techniques re-

construct and model 3D objects or scenes from pho-

tographs or video footage captured in 2D (monocu-

lar) or 3D (stereo). Many of these techniques and ap-

proaches reconstruct objects or scenes with automatic

algorithms from image sequences (Tanskanen et al.,

2013; Pan et al., 2009; Pollefeys et al., 2008; Polle-

feys et al., 2004; Zollh¨ofer et al., 2014; Snavely et al.,

2006). Alongside to these academical approaches

a growing number of commercial software products

have been presented in the last years (Agisoft, 2014;

Autodesk, Inc., 2014; Microsoft Corporation, 2014;

Trimble Navigation Limited, 2014). But would an

architect or an engineer call the resulting 3D recon-

struction a CAD-model? Is a non-expert user able

to apply these tools? Can the user apply the method

without special hardware like a rotating plate, laser,

stereo camera or even a main frame computer? Are

users able to reconstruct models of real world objects

in such a way that they will be able to translate them

back to real world replicas? The answer to these ques-

tions is mostly – No – but why? Therefore, we provide

a literature review of automatic and interactive 3D re-

construction and modeling techniques, compare them

and analyse weaknesses of existing methods. Under

these perspectives, we provide a discussion of how

an interactive computer vision application could be

employed to overcome existing weaknesses. Further,

we develop an idea for an application for the interac-

tive creation of non-monolithic, functioning 3D mod-

els — models that architects or engineers would call

a model, and CAD-ready models that can be applied

for simulation tasks, reverse engineering, replication

and many more purposes.

2 VARIETY OF TECHNIQUES

For 3D reconstruction techniques various research ar-

eas can be identiﬁed. In order to provide a simple

taxonomy, the input data type is used as the identify-

ing header. Monocular and stereo inputs are not sep-

arate, because the stereo advantage is not as signiﬁ-

cant as commonly expected due to the limited stereo

distance of typically less than 5m, the noisy depth

estimation and ﬁeld of view of ∼ 60

◦

(Henry et al.,

2014). The 3D reconstruction techniques and appli-

cations discussed in the following do not necessarily

focus on reconstructing scenes or objects for model-

ing purposes only. Instead, other applications like,

e.g., robot navigation, might be considered. We do

not claim that the list is complete but we argue that

289

Schöning J. and Heidemann G..

Interactive 3D Modeling - A Survey-based Perspective on Interactive 3D Reconstruction.

DOI: 10.5220/0005259402890294

In Proceedings of the International Conference on Pattern Recognition Applications and Methods (ICPRAM-2015), pages 289-294

ISBN: 978-989-758-077-2

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

it contains the relevant work which should be consid-

ered in this context.

2.1 Collections of Photographs

A well known technique that uses a collection

of photographs as input data is Photo tourism by

Snavely et al. (Snavely et al., 2006). This tool uses

an unstructured collection of photographs of a scene,

e.g., acquired from the internet, and converts them to

a sparse 3D model of the scene. Thus the user can

browse, explore and organize large photo collections

in a 3D model of the scene. Camera resections, the

reconstruction of viewpoints from where the photos

are taken, are also possible. Photo tourism recon-

structs the model by computing correspondence fea-

tures of each image using SIFT and RANSAC, which

are based on descriptors that are robust with respect

to variations in pose, scale, and lighting. An opti-

mization algorithm is used to recover the camera pa-

rameters and the 3D position of the feature points. A

robust structure from motion algorithm for 3D struc-

ture estimation is the backbone of Photo tourism.

Some weaknesses of this system are that it works only

with huge collections. Also, textureless or repeating

structures cannot be reconstructed, only continuous

scenes. Also, the resulting model is quite sparse.

Inspired by Photo tourism, the software Microsoft

Photosynth (Microsoft Corporation, 2014) is a tool

for capturing, viewing and sharing photos in 3D. It

works in the same way as Photo tourism and au-

tomatically reconstructs a collection of overlapping

photographs into a 3D model. A scene of 200 pho-

tographs is computed in ﬁve to ten minutes on an av-

erage laptop. To overcome the drawbacks of Photo

tourism, a photography guide is provided which helps

the user with how to take photos such that Photosynth

can be used to best advantage. The guide provides

hints such as to not shoot repetitions, complex occlu-

sions, or shiny objects. For well made collections, ac-

ceptable models can be obtained. Moving or dynamic

objects cannot handled by Photosynth.

The main purpose of the ﬁrst two techniques is

browsing, exploring and organizing photographic col-

lections in a 3D model. The purpose of Agisoft Pho-

toScan (Agisoft, 2014) is the automatic creation of

textured 3D models from photographs. For a success-

ful and complete reconstruction, photographs shown

in a 360

◦

view of the object are required. It depends

on the viewpoints whether the software works auto-

matically or interactively, because the user is required

to outline the object of interest manually in the re-

construction process. By this means, irrelevant el-

ements such as the background or the turntable are

excluded. Further processing steps like aligning pho-

tographs, building a dense point cloud, meshing point

clouds and creating textured surfaces work automati-

cally in PhotoScan. Like Photosynth, Agisoft Photo-

Scan comes with a guideline, also, its drawbacks are

similar.

Like PhotoScan, Autodesk 123D Catch (Au-

todesk, Inc., 2014) creates 3D models from a col-

lection of photographs and like PhotoScan it builds

a monolithic 3D model. But in 123D Catch, the user

can manipulate the model in a post-processing phase.

To increase speed, the overall process is outsourced to

cloud computing. Like the other systems, 123D Catch

can not handle moving objects, reﬂections, under- or

overexposure, blurred photographs, occlusions etc.

Kowdle et al. (Kowdle et al., 2014) addressed

these issues of automatic reconstruction for object

creation by a semi-automatic approach. In this ap-

proach, the user is in the loop of computational re-

construction, which allows the user to intuitively in-

teract with the algorithm. The user guides the pro-

cess of image-based modeling to ﬁnd a model of the

object of interest by interacting with the nodes of

the graph. Thus, the 3D reconstruction achieves a

much higher quality in an acceptable time span, espe-

cially for scenes with textureless surfaces and struc-

tural cues. This semi-automatic approach works with

multiple images of a scene, captured from differ-

ent viewpoints. In the pre-processing steps a struc-

ture from motion algorithm creates a dense 3D point

cloud. This cloud is used for growing 3D superpix-

els. With user interaction the superpixels are labeled

to segments and ﬁnally to the object of interest. Using

this knowledge, a RANSAC based plan-ﬁtting on the

labeled 3D points estimates the 3D Model.

2.2 Single Photograph

Photo collections acquired according to guidelines for

3D reconstruction are rare. Single photos or very

small collections of photos are more common. De-

bevec et al. (Debevec et al., 1996) presented an early

approach to hybrid modeling, that can model and ren-

der existing architectural scenes from a sparse set of

still photographs. But to extract a model from a small

collection requires addition information. The Fac¸ade

software combines an interactive image-guided geo-

metric modeling tool with model-based stereo match-

ing and a view-dependent texture mapping. In the

interactive modeling phase the user selects block el-

ements and aligns their edges with visible edges in

the input images. The system then automatically

computes the dimensions and locations of the blocks

along with the camera resection. Based on the as-

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sumption that man-made architecture relies on geo-

metric elements, this approach reconstructs more re-

liable models from geometric primitives.

Upon this principle the plug-in Match Photo for

the Trimble Navigation Limited tool SketchUp pro-

vides a manual method for creating a 3D model from

one photograph or match an existing CAD-model into

a photograph. After calibrating the photo to the coor-

dination system of the SketchUp workspace, the user

draws the edges of the object over the photograph.

When all visual and occluded edges are drawn, the 3D

model is built and can be texturized (Trimble Naviga-

tion Limited, 2014).

Another approach we want to mention is the re-

construction of building interiors by Furukawa and

Szeliski (Furukawa et al., 2009) because it is able to

handle textureless planar surfaces such as uniformly-

painted.

2.3 Video Footage

An automatic 3D reconstruction method from video

footage described by Pollefeys et al. (Pollefeys et al.,

2008) produces a dense, geo-registered 3D model of

an urban scene in real-time. The video footage is cap-

tured by a multi-camera system in conjunction with

INS (Inertial Navigation System) and GPS measure-

ments. To achieve real-time they decouple the prob-

lem into the reconstruction of depth maps from sets of

images followed by the fusion of these depth maps, a

simple and fast algorithm that can be implemented on

GPUs. It yields a compact and geometrically consis-

tent representation of the 3D scene.

ProFORMA (Probabilistic Feature-based Online

Rapid Model Acquisition) (Pan et al., 2009) recon-

structs freely rotated objects in front of a ﬁxed-

position video camera in near real-time. Due to the

fact that the system guides the user with respect to

the manipulation, i.e., the rotation of the object, this

method might not be called an entirely automatic

method. The user rotates the textured object in front

of the camera. The ﬁnal model is produced by Delau-

nay tetrahedralization of a point cloud obtained from

online structure from motion estimation, followed by

a probabilistic tetrahedron carving step to obtain a

textured surface mesh of the object. A key-frame for

the reconstruction is taken when a sufﬁciently large

rotation is detected. During the initialization phase a

photo of the background is captured for background

removal.

VideoTrace (van den Hengel et al., 2007) is a sys-

tem for interactive generation of realistic 3D mod-

els of objects from video. These models can be in-

serted into a simulation environment or another appli-

cation. The user interacts with VideoTrace by tracing

the shape of the object over one or more frames of

the video. Immediate feedback mechanisms allow the

user to rapidly model those parts of the scene which

are of interest, up to the required level of detail. The

combination of automated and manual reconstruction

allows VideoTrace to model parts of the scene which

are not visible, and to succeed in cases where purely

automated approaches would fail. Before any interac-

tive modeling takes place, structure and motion anal-

ysis is carried out on the video sequence to recon-

struct a sparse set of 3D scene points. The resulting

3D point cloud is “drawn” over the frames of the in-

put sequence. The interaction is done by modeling

primitives. By default, these primitives (e.g., traced

lines or curves) are automatically reﬁned in 2D by ﬁt-

ting to local strong superpixel boundaries, so a user

neither needs artistic abilities, nor is “pixel perfect”

tracing required. The interaction process occurs in

real time. This allows the user to switch between im-

ages from the original sequence naturally, selecting

the most appropriate view of the scene at each stage.

The model can be rendered using texture maps ob-

tained from frames of the video.

Generating dense 3D maps of indoor environ-

ments using a RGB-D camera has been proposed by

Henry et al. (Henry et al., 2014), despite the lim-

ited depth precision and ﬁeld of view such cameras

can provide. This technique effectively combines the

visual and shape information of a RGB-D camera.

Aligning the current frame to the previous frame with

an enhanced iterative closest point algorithm com-

bines the RGB and the D information. The resulting

feature point cloud in 3D is visualized in surfels (sur-

face patches).

Further reconstruction methods to be considered

are automated reconstruction of buildings using a

hand held video camera (Fulton and Fraser, 2009),

live metric 3D reconstruction on mobile phones (Tan-

skanen et al., 2013), in situ image-based modeling

(van den Hengel et al., 2009) and visual modeling

with a hand-held camera (Pollefeys et al., 2004), but

can not be described in this short paper.

3 DIRECT COMPARISON

By comparing all techniques mentioned in Section 2

yield, Table 1. It shows a comparison of most signif-

icant techniques (top half) and commercial software

products (lower half).

The ﬁrst ﬁnding of the comparison is that there is

a very strong correlation between the input and the

execution mode of techniques. For a huge collec-

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Table 1: Direct comparison of signiﬁcant techniques and commercial software products for 3D reconstruction and modeling.

Main purpose Input Output

Mode

Recording

equipment

Handling of texture

-less, shiny etc. objects

Initial-

ization

Modelling architecture

(Debevec et al., 1996)

model acquisition for

man-made objects and

scenes

single photograph or

collection of pho-

tographs

monolithic 3D model of

the object or scene

i monocular

camera

possible but should be

prevented

Photo tourism (Snavely

et al., 2006)

browsing, exploring and

organizing photo collec-

tions in a sparse 3D the

scene

huge collection of pho-

tographs (> 100 photos)

sparse 3D model of the

scene

a monocular

camera with

or without

GPS device

hard – photographs

should not have re-

peating or textureless

structures

VideoTrace (van den

Hengel et al., 2007)

model acquisition for

scenes

video sequence monolithic 3D model of

the scene or part of the

scene

i monocular

video

camera

not mention in the article n

ProFORMA (Pan et al.,

2009)

reconstruction of objects uncut video sequence

with static background

monolithic model of a

object

a monocular

video cam-

era on

tripod and

a rotatable

object

hard – object must be

sufﬁciently textured

Real-time urban 3D re-

construction (Pollefeys

et al., 2008)

reconstruction of urban

scenes

uncut video sequence

with a ﬁx orientation

dense 3D model of the

scene

a monocular

multi-

camera

system

not mention in the article r

User in the Loop for

Image-Based Modeling

(Kowdle et al., 2014)

model acquisition for

objects

single photograph or

collection of photograph

monolithic 3D model of

the object

i monocular

camera

handled by the user n

RGB-D modeling of

indoor environments

(Henry et al., 2014)

3D indoor mapping uncut video sequence in

RGB-D

3D map of the scene for,

e.g. robot navigation

a active stereo

camera

handled by the D-

channel of the active

camera

123D CATCH (Au-

todesk, Inc., 2014)

model acquisition for

objects

collection of pho-

tographs (> 20 photos

and < 70 photos)

monolithic 3D model of

the object

a monocular

camera,

cloud

computing

hard – photographs

should not have oc-

clusion, repetition,

shininess etc.

Agisoft PhotoScan (Ag-

isoft, 2014)

model acquisition for

objects

collection of pho-

tographs (> 10 photos

and < 70 photos)

monolithic 3D model of

the object

a monocular

camera

hard – photographs

should not have oc-

clusion, repetition,

shininess etc.

Photosynth (Microsoft

Corporation, 2014)

browsing, exploring and

organizing photo collec-

tions in a sparse 3D

scene

collection of pho-

tographs (> 10 photos)

sparse 3D model of the

scene

a monocular

camera

hard – photographs

should not have oc-

clusion, repetition,

shininess etc.

SketchUp Match Photo

(Trimble Navigation

Limited, 2014)

model acquisition for

objects

single photograph or

collection of photograph

3D model of the object m monocular

camera

handled by the user r

execution mode: automatic, interactive, manual;

Initialization: required, not required; *depends on the RGB-D camera

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tion of photographs or video footage without many

outliers, available automatic reconstruction seems to

work quite well. In all other cases, interactive or man-

ual methods perform better.

Almost all techniques require speciﬁc collections.

The collections must be well planned photographs

or videos that show the object of interest in a 360

◦

view. Many other required properties, like an overlap

by 50% of corresponding photographs, are described

in the guidelines of each method. According to the

guidelines, all automatic and almost all other tech-

niques have problems with, reﬂection, occlusion and

repeating structures. The output of all techniques is a

monolithic model without any declaration of subparts.

In most cases, the monolithic model is no more than

a meshed dense point cloud. Such a model cannot be

used in CAD applications, e.g., for simulation tasks,

reverse engineering or creation of functioning repli-

cas. Further information, e.g., if a technique needs

special hardware equipment, can be found in Table 1.

4 PERSPECTIVE

After the direct comparison, drawbacks and missing

elements of existing 3D modeling techniques can be

spotted. It becomes obvious, that due to missing in-

formation like occlusions, automatic 3D reconstruc-

tion can only achieve acceptable results if enough

photographs of the scene or knowledge of the under-

lying model are available. As pointed out in (Kow-

dle et al., 2014; van den Hengel et al., 2007; De-

bevec et al., 1996) interactive methods include real

world model knowledge of the user into the recon-

struction process. This enables a reconstruction from

a few or even from a single photograph. Currently,

the necessary real world knowledge cannot be ex-

plicitly integrated into existing algorithms, due to the

sheer complexity it would create. Interactive model-

ing techniques, however, are the only way to reliably

reconstruct 3D models from any collection of pho-

tographs or video footage. 3D reconstruction from

arbitrary and unplanned collections will broaden the

range of applications, examples are reverse engineer-

ing of mechanical parts, animal reconstruction for bi-

ology (e.g., for arachnology) and historical urban re-

construction.

Figure 1 outlines our ideas for an interactive 3D

reconstruction architecture. It consists of three main

parts: i) the input data in the form of monocular or

stereo photographs / video footage, as well as addi-

tional data like physical interrelationships, ii) the in-

teractivereconstruction process and iii) the user as do-

main expert of the real world. The interactive recon-

visualization

supervised/active

learner

reconstruction

domain

knowledge of

real world

data

interactive

reconstruction process

3D model

(manufacturable)

Figure 1: Interactive reconstruction architecture.

struction process should join the computational power

of today’s computers with the conceptual knowledge

of the user to solve issues that are computationally

unfeasible up to now. In contrast to most interactive

3D reconstruction methods, the computer remains the

“work horse” of the process, while supervised and ac-

tive learning algorithms shift the load from the user to

the computer.

An application based on this architecture should

be able to reconstruct from any collections of pho-

tographs and video footage or archive photos because

missing information will be added by user knowledge.

To achieve a better understanding of already exist-

ing techniques, algorithms, methods and processing

pipelines, we describe a possible application based on

the interactive architecture. Based on a photograph,

a collection of photographs, a video or a collection

of video, the user starts the interactive reconstruction

process. In the ﬁrst step, a monolithic model of the

object or scene of interest should be created. There-

fore, the user has to identify the object of interest with

a marker. It should not be necessary to mark or out-

line the objects in every frame as it is common prac-

tice to date (e.g. (Agisoft, 2014)). The setting of a

marker triggers the automatic calculation of a point

cloud model in real-time. If the user recognizes prob-

lems in the point cloud, the user can exclude problem-

atic items, such as reﬂections, with another marker

from the automatic process, or directly modify the

point cloud or the meshed point cloud. This direct

modiﬁcation allows the user to add occluded infor-

mation to the 3D model or to delete projection errors

in the 3D model. Once a monolithic 3D model is

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reconstructed with a desired level of detail, the next

step is to break down the model to its components or

subparts until every subpart itself is monolithic. For

breaking the monolithic 3D model into its subparts,

the user roughly scribbles each subpart on the input

data or uses common 3D breaking down techniques

like cut-planes directly on the model. If all subparts

have been identiﬁed, the user has to model the con-

nections between all parts. In addition to pre-deﬁned

types of connections like glueing or screwing, this

step has to account for moveable connections, such as

a ball joint, where the user adds speciﬁc information

like rotation axes, maximum angles etc. In the next

step, the user assigns a material to each subpart, and

an automatic consistency check should be included to

ensure the compatibility of connection types and ma-

terials. The last step is the export of this model to

common CAD format like *.dxf.

Such an application enables full CAD from recon-

structed 3D models. This kind of CAD-ready 3D re-

construction can be used for simulation, reverse engi-

neering, modeling, inverse modeling, testing, labeling

and analysis tasks. The ultimate goal is that architects

and engineers will accept and call the models out of

3D reconstruction — a model.

5 CONCLUSION

The literature review of 3D reconstruction, compari-

son and the discussion of a possible application have

shown that interactive 3D reconstruction is able to

create CAD-ready model — not just dense or sparse

models. We agree with (Kowdle et al., 2014; van den

Hengel et al., 2007; Debevec et al., 1996) that a fully

automatic reconstruction for high quality object cre-

ation is currently not feasible. To show the ability of

interactive 3D reconstruction we started to implement

the propsed methods. We expect the identiﬁcation

of even more weaknesses of current 3D reconstruc-

tion and computer vision methods in the course of re-

search conducted in the proposed directions. Finally,

we are optimistic that an interactive3D reconstruction

tool could create models of real world objects which

can be “translated” back to the real world with 3D

printers and CNC-machines or can be used for many

CAD tasks.

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