Extracting Sharp Features from RGB‐D Images

Sharp edges are important shape features and their extraction has been extensively studied both on point clouds and surfaces. We consider the problem of extracting sharp edges from a sparse set of colour‐and‐depth (RGB‐D) images. The noise‐ridden depth measurements are challenging for existing featu...

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Bibliographic Details
Published in:Computer graphics forum Vol. 36; no. 8; pp. 138 - 152
Main Authors: Cao, Y‐P., Ju, T., Xu, J., Hu, S‐M.
Format: Journal Article
Language:English
Published: Oxford Blackwell Publishing Ltd 01.12.2017
Subjects:
Color
Consolidation
Feature extraction
Galling
I.3.3 [Computer Graphics]: Picture/Image Generation — Digitizing and Scanning I.3.5 [Computer Graphics]: Computational Geometry and Object Modelling — Curve, surface, solid, and object representations
modelling
object scanning/acquisition
point‐based graphics
ISSN:0167-7055, 1467-8659
Online Access:Get full text
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Summary:Sharp edges are important shape features and their extraction has been extensively studied both on point clouds and surfaces. We consider the problem of extracting sharp edges from a sparse set of colour‐and‐depth (RGB‐D) images. The noise‐ridden depth measurements are challenging for existing feature extraction methods that work solely in the geometric domain (e.g. points or meshes). By utilizing both colour and depth information, we propose a novel feature extraction method that produces much cleaner and more coherent feature lines. We make two technical contributions. First, we show that intensity edges can augment the depth map to improve normal estimation and feature localization from a single RGB‐D image. Second, we designed a novel algorithm for consolidating feature points obtained from multiple RGB‐D images. By utilizing normals and ridge/valley types associated with the feature points, our algorithm is effective in suppressing noise without smearing nearby features. Sharp edges are important shape features and their extraction has been extensively studied both on point clouds and surfaces. We consider the problem of extracting sharp edges from a sparse set of colour‐and‐depth (RGB‐D) images. The noise‐ridden depth measurements are challenging for existing feature extraction methods that work solely in the geometric domain (e.g. points or meshes). By utilizing both colour and depth information, we propose a novel feature extraction method that produces much cleaner and more coherent feature lines. We make two technical contributions. First, we show that intensity edges can augment the depth map to improve normal estimation and feature localization from a single RGB‐D image. Second, we designed a novel algorithm for consolidating feature points obtained from multiple RGB‐D images. By utilizing normals and ridge/valley types associated with the feature points, our algorithm is effective in suppressing noise without smearing nearby features.
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.13069