nakagawa masafumi : [email protected] institute of technology panel on 3d data...

NAKAGAWA Masafumi : [email protected] INSTITUTE OF TECHNOLOGY

Panel on 3D data collection and reconstruction

Chair: Masafumi Nakagawa, Shibaura Institute of Technology

Thursday, 12th, 13:30-14:30

Sander Oude Elberink, University of Twente (via Adobe)

Martin Tamke, CITA, KADK (via Adobe)

Alain Lapierre, Bentley

Susanne Becker, Stuttgart University

[email protected]

[email protected]

[email protected]

[email protected]

[email protected]

The Royal Danish Academy of Fine Arts, Schools of Architecture, Design and Conservation

(3D reconstruction)

(BIM, Indoor localization)

(3D modeling and design)

(Laser scanning, 3D reconstruction)

(3D modeling, Seamless positioning)


Panel on 3D data collection and reconstruction

What is the biggest challenge in Indoor 3D data collection and reconstruction (2014-2020)

- Question 1

- Question 2

- Question 3

- Questions from audiences

- Introduction

10 min

10 min

10 min

15 min

5 min

- Summary 10 min


Indoor mapping and modeling (IMM)

Sensors

Devices

Data structures

Acquisition

Modeling

Navigation

Visualization

Applications

Legal Issues and StandardsAlgorithms

Solving problems in one field of IMM benefits other fields of IMM


3D data collection and reconstruction Sensing and modeling are also required for indoor mapping

CAD data

CityGML 　（ City Geography Markup Language ）

LOD1 LOD2 LOD3 LOD4LOD0Box Box, roof, facade Outside Inside

Standard, http://portal.opengeospatial.org/files/?artifact_id=28802

- Point cloud

Data reduction

Modeling LOD4 ～ 5 ?

- Moving object

- Changing object

- Images

Sensing


Point based rendering 　＋　 spatial interpolation

Improved “point cloud” to be used as “a panorama image”

INPUT （ colored points ）

OUTPUT （ colored points ）

Side looking

Transparent effect

“Near” from a viewpoint ＝ Sparse points“Far” from a viewpoint ＝ Dense points

Near-far problem

Hidden points are visible among near-side points


3D scanned “Gunkanjima” with Terrestrial LiDAR

VZ-400 （ RIE

GL ）

800 million pts, 65 view points

Data source : - Keisoku Research Consultant. Co.,ltd.- Nagasaki University

- Nagasaki CityRendering : - Shibaura Institute of Tech.


PROBLEMS IN INDOOR MAPPING AND MODELLING

Security and levels of access

Privacy

Copyright

The diversity of indoor

environments

Unification of outdoor and

indoor models

Gaming

Industrial applications

Natural description of

indoor environments

Augmented systems

Indoor modelling for

crisis response

Optimal routing

Navigation queries and

multiplicity of targets

Travelling imperatives

Automated space

subdivision

Navigation models

Real-time decision support

Discrete vs continuous navigation

models

Real-time change

visualization

Complexity visualization

Aural cues

PoI and landmarks strategies

Web and mobile devices

Real-time modelling

Dynamic abstraction

Discovering the context of

space

Diversity of Indoor

Environments

Software tool

Integration with GIS/BIM

Sensor fusion

Mobility

Real-time acquisition of

dynamic environments

Variable occupancy, automated

feature removal

Variable lighting

conditions

Learning the composition of

space

Acquisition and Sensors

Existing problems

Emerging problems

Data Structures

and Modelling

Visualization Navigation ApplicationsLegal Issues

and Standards


Objectives

Still camera, Video camera, Stereo, Tablet PC, Terrestrial scanner, Mobile scanner, etc.

①The best device for data acquisition ?

Real-time acquisition of dynamic environments,

②Real-time processing is required ?

Professionals vs. Cloud sourcing vs. Full-automation

③The best data collector and modeler ?

Real-time modelling

Dynamic abstraction


space

Diversity of Indoor

Environments

Software tool


Sensor fusion

Mobility




feature removal

Variable lighting

conditions


space


Data Structures

and Modelling

Real-time modeling, Dynamic abstraction

Accuracy, Cost, etc.

Cost, Reliability, Integrity, etc.


Discovering the context of space


①The best device for data acquisition3D data acquisition without GNSS

- Scanner (+Registration)

- Camera

- Mobile scanner

- Photogrammetry

- Structure from Motion

- Terrestrial LiDAR

- Kinect, TOF imager

- Line scanner + IMU

× Manual editing works

× Moving objects

× Cost

× Measurement distance

× Cost

○ Cost

○ Cost, Speed

○ Speed

○ Speed

○ Speed

What is the best device for indoor data acquisition ?


①The best device for data acquisition

Sander

Martin

Alain

Susanne

What is the best device for indoor data acquisition ?

- Knowledge and indoor traces

- Depends on the targeted tasks, required fidelity/accuracy and costs.

- 3d scannner, potentially lightweight Lidar

- Currently: Terrestrial laser scanner

- For maintenance/operation purposes, panoramic photos might be sufficient.- For navigation, panoramic photos and/or Laser Scanner.

- For fidelity/measurement, combination of Laser Scanner (today static, future mobile) and Photos.

- Future (after 2014?): mobile devices (laser scanner). A device that someone can carry in their hand, on their head etc.

- Reason: currently the registration of mobile acquired point/image data is not good enough.


②Real-time processingReal-time processing is required ?

- Scene

- Applications

- Objects

- BIM- LBS

- Public space- Work space

- Passageway

- Autonomous robots

Station, Shopping mall, Exhibition hall, AirportFactory/Plant, Office, School/University

- Private space Your house

- Shopping mall

- Room Wall, Floor, Ceiling, Window, Table, Chair, Electric appliances, etc.

Signboard, Goods, Price tags, etc.Wall, Floor, Ceiling, Window, Walker, etc.

Infrastructure managementRoute planning, Navigation, Dynamic abstractionNavigation, Dynamic environment recognition

What is your interested real-time processing application(s) ?

- Factory Machine, Parts, Pipeline, etc.


②Real-time processing

Sander

Martin

Alain

Susanne

What is your interested real-time processing application(s) ?

- Real-time acquisition and modeling of dynamic environments

- For augmented reality, need to register in real-time the reality with the virtual model

- Detection of spaces and 3d objects and their semantics

- Route planning, evacuations.

For evacuations it is necessary to capture the current situation, and adapt to that. So a fast, post event dataset is required.

- During construction, progress monitoring and last minute changes.


③The best data collector and modelerProfessionals vs. Cloud sourcing vs. Full-automation

- Cloud sourcing

- Professionals

- Full-automation

○ Reliability× Resource

○ Resource× Reliability

○ Speed

→ Tools, Software, Freeware ?

→ Cost ?

× Integrity→ Software, Algorithm ?

Who is the best data collector and modeler ?

(e.g. OpenStreetMap)

# Indoor field is larger than outdoor

# Volunteers are nonprofessionals

# 100 % success rate ?


③The best data collector and modeler

Sander

Martin

Alain

Susanne

Who is the best data collector and modeler ?

- Automatic modeling from crowd sourced data

- Depends on disciplines and user needs. Being a software provider, we are aiming at providing the best software, but the competition and open source community is doing a great job too. Crowd sourcing could apply for less specialized and rigorous tasks.

- today: Professional users

- The one that best fits the users' needs. How well are the users' needs defined? Maybe the user can best process the data, if he/she is equipped with a variety of interactive tools.

- future: algorithms augmented by Professional users


Questions from audiences

Still camera, Video camera, Stereo, Tablet PC, Terrestrial scanner, Mobile scanner, etc.

①The best device for data acquisition ?

Real-time acquisition of dynamic environments,

②Real-time processing is required ?

Professionals vs. Cloud sourcing vs. Full-automation

③The best data collector and modeler ?

Real-time modelling

Dynamic abstraction


space

Diversity of Indoor

Environments

Software tool


Sensor fusion

Mobility




feature removal

Variable lighting

conditions


space


Data Structures

and Modelling

Real-time modeling, Dynamic abstraction

Accuracy, Cost, etc.

Cost, Reliability, Integrity, etc.

Discovering the context of space



Summary

Sander

Martin

Alain

Susanne


- Semantic interpretation

- Automated processing of huge volume of data (data transfer, feature extraction, semantic interpretation)

- Gaining comprehensive data with semantic information

- Comment

nakagawa masafumi : [email protected] institute of technology panel on 3d data...

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