Publicação: Extraction of building roof planes with stratified random sample consensus
Carregando...
Data
Autores
Orientador
Coorientador
Pós-graduação
Curso de graduação
Título da Revista
ISSN da Revista
Título de Volume
Editor
Tipo
Artigo
Direito de acesso
Acesso restrito
Resumo
This paper describes a consensus-set estimation for building roof-plane detection using a stratified random sample consensus (sRANSAC) algorithm applied to point clouds acquired by laser scanning systems. The main idea is to use one initial classification to generate consensus-set candidates to optimise the sampling mechanism compared to the original RANSAC. The initial classification is performed using mathematical morphology to filter ground returns and estimate local variance information to detect potential planar regions. Thus, the algorithm can prioritise points within planar segments and the number of iterations can be estimated dynamically from available data. The results based on experiments using five different lidar datasets indicate that the proposed method reduces the number of computations for building roof-plane detection and also improves accuracy compared to RANSAC.
Descrição
Palavras-chave
lidar data, local variance information, mathematical morphology, plane extraction, RANSAC, stratified random sample consensus
Idioma
Inglês
Como citar
Photogrammetric Record, v. 33, n. 163, p. 363-380, 2018.
