2 Citations (Scopus)
23 Downloads (Pure)

Abstract

Layered Depth Images (LDI) compactly represent multiview images and videos and have widespread usage in image-based rendering applications. In its typical use case scenario of representing a scanned environment, it has proven to be a less costly alternative than separate viewpoint encoding. However, higher quality laser scanner hardware and different user interaction paradigms have emerged, creating scenarios where traditional LDIs have considerably lower efficacy. Wide-baseline setups create surfaces aligned to the viewing rays producing a greater amount of sparsely populated layers. Free viewpoint visualization suffers from the variant quantization of depths on the LDI algorithm, reducing resolution of the dataset in uneven directions. This paper presents an alternative representation to the LDI, in which each layer of data is positioned in different viewpoints that coincide with the original scanning viewpoints. A redundancy removal algorithm based on world-space distances as opposed to to image-space is discussed, ensuring points are evenly distributed and are not viewpoint dependent. We compared our proposed representation with traditional LDIs and viewpoint dependent encoding. Results showed the multiview LDI (MVLDI) creates a smaller number of layers and removes higher amounts of redundancy than traditional LDIs, ensuring no relevant portion of data is discarded in wider baseline setups.
Original languageEnglish
Pages (from-to)115-122
Number of pages8
JournalJournal of WSCG
Volume25
Issue number2
Publication statusPublished - 1 Jan 2017

Fingerprint

Redundancy
Visualization
Baseline
Scanning
Hardware
Encoding
Lasers
Image-based Rendering
Laser Scanner
Scenarios
Image Space
Dependent
Alternatives
User Interaction
Use Case
Efficacy
Half line
Quantization
Paradigm

Keywords

  • Image-based representation
  • Point clouds
  • Video-based rendering

Cite this

@article{379e4352558c4ebba7290a12640015b5,
title = "Multiview layered depth image",
abstract = "Layered Depth Images (LDI) compactly represent multiview images and videos and have widespread usage in image-based rendering applications. In its typical use case scenario of representing a scanned environment, it has proven to be a less costly alternative than separate viewpoint encoding. However, higher quality laser scanner hardware and different user interaction paradigms have emerged, creating scenarios where traditional LDIs have considerably lower efficacy. Wide-baseline setups create surfaces aligned to the viewing rays producing a greater amount of sparsely populated layers. Free viewpoint visualization suffers from the variant quantization of depths on the LDI algorithm, reducing resolution of the dataset in uneven directions. This paper presents an alternative representation to the LDI, in which each layer of data is positioned in different viewpoints that coincide with the original scanning viewpoints. A redundancy removal algorithm based on world-space distances as opposed to to image-space is discussed, ensuring points are evenly distributed and are not viewpoint dependent. We compared our proposed representation with traditional LDIs and viewpoint dependent encoding. Results showed the multiview LDI (MVLDI) creates a smaller number of layers and removes higher amounts of redundancy than traditional LDIs, ensuring no relevant portion of data is discarded in wider baseline setups.",
keywords = "Image-based representation, Point clouds, Video-based rendering",
author = "Pereira, {Jo{\~a}o Madeiras} and Gaspar, {Jos{\'e} Ant{\'o}nio} and Carla Fernandes and Anjos, {Rafael Kuffner dos}",
year = "2017",
month = "1",
day = "1",
language = "English",
volume = "25",
pages = "115--122",
journal = "Journal of WSCG",
issn = "1213-6972",
publisher = "Vaclav Skala Union Agency",
number = "2",

}

Multiview layered depth image. / Pereira, João Madeiras; Gaspar, José António; Fernandes, Carla; Anjos, Rafael Kuffner dos.

In: Journal of WSCG, Vol. 25, No. 2, 01.01.2017, p. 115-122.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Multiview layered depth image

AU - Pereira, João Madeiras

AU - Gaspar, José António

AU - Fernandes, Carla

AU - Anjos, Rafael Kuffner dos

PY - 2017/1/1

Y1 - 2017/1/1

N2 - Layered Depth Images (LDI) compactly represent multiview images and videos and have widespread usage in image-based rendering applications. In its typical use case scenario of representing a scanned environment, it has proven to be a less costly alternative than separate viewpoint encoding. However, higher quality laser scanner hardware and different user interaction paradigms have emerged, creating scenarios where traditional LDIs have considerably lower efficacy. Wide-baseline setups create surfaces aligned to the viewing rays producing a greater amount of sparsely populated layers. Free viewpoint visualization suffers from the variant quantization of depths on the LDI algorithm, reducing resolution of the dataset in uneven directions. This paper presents an alternative representation to the LDI, in which each layer of data is positioned in different viewpoints that coincide with the original scanning viewpoints. A redundancy removal algorithm based on world-space distances as opposed to to image-space is discussed, ensuring points are evenly distributed and are not viewpoint dependent. We compared our proposed representation with traditional LDIs and viewpoint dependent encoding. Results showed the multiview LDI (MVLDI) creates a smaller number of layers and removes higher amounts of redundancy than traditional LDIs, ensuring no relevant portion of data is discarded in wider baseline setups.

AB - Layered Depth Images (LDI) compactly represent multiview images and videos and have widespread usage in image-based rendering applications. In its typical use case scenario of representing a scanned environment, it has proven to be a less costly alternative than separate viewpoint encoding. However, higher quality laser scanner hardware and different user interaction paradigms have emerged, creating scenarios where traditional LDIs have considerably lower efficacy. Wide-baseline setups create surfaces aligned to the viewing rays producing a greater amount of sparsely populated layers. Free viewpoint visualization suffers from the variant quantization of depths on the LDI algorithm, reducing resolution of the dataset in uneven directions. This paper presents an alternative representation to the LDI, in which each layer of data is positioned in different viewpoints that coincide with the original scanning viewpoints. A redundancy removal algorithm based on world-space distances as opposed to to image-space is discussed, ensuring points are evenly distributed and are not viewpoint dependent. We compared our proposed representation with traditional LDIs and viewpoint dependent encoding. Results showed the multiview LDI (MVLDI) creates a smaller number of layers and removes higher amounts of redundancy than traditional LDIs, ensuring no relevant portion of data is discarded in wider baseline setups.

KW - Image-based representation

KW - Point clouds

KW - Video-based rendering

UR - http://www.scopus.com/inward/record.url?scp=85027255830&partnerID=8YFLogxK

M3 - Article

VL - 25

SP - 115

EP - 122

JO - Journal of WSCG

JF - Journal of WSCG

SN - 1213-6972

IS - 2

ER -