NDLI: Closing gaps by clustering unseen directions

Content Provider	IEEE Xplore Digital Library
Author	Impoco, G. Cignoni, P. Scopigno, R.
Copyright Year	2004
Description	Author affiliation: Ist. di Scienza e Tecnologie dell'Informazione, Consiglio Nazionale delle Ricerche, Pisa, Italy (Impoco, G.; Cignoni, P.; Scopigno, R.)
Abstract	Although in recent years the 3D-scanning field has reached a good level of maturity, it is still far from being perceived by common users as a 3D-photography approach, as simple as standard photography is. The main reason for that is that obtaining good 3D models without human intervention is still very hard. In particular, two problems remain open: automatic registration of single shots and planning of the acquisition session. In this paper we address the second issue and propose a solution to improve the coverage of automatically acquired objects. Rather than searching for the next-best-view in order to minimise the number of acquisitions, we propose a simple and easy-to-implement algorithm limiting our scope to closing gaps (i.e. filling unsampled regions) in roughly acquired models. The idea is very simple: detect holes in the current model and cluster their estimated normals in order to determine new views. Some results are shown to support our approach.
Starting Page	307
Ending Page	316
File Size	1635937
Page Count	10
File Format	PDF
ISBN	0769520758
DOI	10.1109/SMI.2004.1314518
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-07
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sampling methods Shape Photography Humans Clustering algorithms Filling Spatial resolution Computer vision Software quality Quality management
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Full-frame video stabilization

Closing gaps by clustering unseen directions (2004)

Multi-resolution shape description by corners

Object removal by exemplar-based inpainting

Layered graph matching by composite cluster sampling with collaborative and competitive interactions

Spatial Landcover Classification Using Fuzzy Inference

Quantization error in spatial sampling: comparison between square and hexagonal pixels

Unsupervised Clustering using Multi-Resolution Perceptual Grouping

Spatial Clustering Algorithms and Quality Assessment

Closing gaps by clustering unseen directions

Similar Documents

Full-frame video stabilization

Closing gaps by clustering unseen directions (2004)

Multi-resolution shape description by corners

Object removal by exemplar-based inpainting

Layered graph matching by composite cluster sampling with collaborative and competitive interactions

Spatial Landcover Classification Using Fuzzy Inference

Quantization error in spatial sampling: comparison between square and hexagonal pixels

Unsupervised Clustering using Multi-Resolution Perceptual Grouping

Spatial Clustering Algorithms and Quality Assessment

Closing gaps by clustering unseen directions