A Comparative Analysis of Depth-Discontinuity and Mixed-Pixel Detection Algorithms

Tang, Pingbo; Huber, Daniel; Akinci, Burcu

doi:10.1109/3dim.2007.5

Cited by 38 publications

(31 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The impact of object reflectance on ghost points is verified, as in [18,20,21]. The effect on gpr if scanning resolution increases is comparable to the effect on gpr if objects are larger.…”

Section: Discussionmentioning

confidence: 74%

“…The effect of these variables on the occurrence of ghost points have been comprehensively studied and discussed in [9,12,[15][16][17][18]. Previous research has focused on understanding the sources of error in the sensing and modeling process [18][19][20]. Similarly, other studies have aimed to characterize laser instruments and analyze the effect of various operating parameters in order to identify edges and remove the unwanted data points by means of e.g., two-dimensional edge-detection processes [21], and algorithms to detect depth discontinuity and mixed pixels in 3D data [20,22].…”

Section: Introductionmentioning

confidence: 99%

“…Previous research has focused on understanding the sources of error in the sensing and modeling process [18][19][20]. Similarly, other studies have aimed to characterize laser instruments and analyze the effect of various operating parameters in order to identify edges and remove the unwanted data points by means of e.g., two-dimensional edge-detection processes [21], and algorithms to detect depth discontinuity and mixed pixels in 3D data [20,22]. A manual selection and correction of the point cloud during the TLS data preprocessing procedure has also been tested [9], but this technique is not efficient to delete ghost points on large datasets (e.g., real forest canopies).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Correction of Erroneous LiDAR Measurements in Artificial Forest Canopy Experimental Setups

Cifuentes

Zande

Salas

et al. 2014

Forests

View full text Add to dashboard Cite

Terrestrial laser scanning (TLS) data makes possible to directly characterize the three-dimensional (3D) distribution of canopy foliage elements. The scanned edges of these elements may result in incorrectly point measurements (i.e., "ghost points") impacting the quality of point cloud data. Therefore, estimation of the ghost points' spatial visibilities, measurement of their characteristics and their removal are essential. In order to quantify the improvements on data quality, a method is developed in this study to efficiently correct for ghost points. Since the occurrence of ghost points is governed by a number of factors, (e.g., scanning resolution and distance, object properties, incident angle); the developed method is based on the analysis of the effects of these factors under controlled conditions where canopy-like objects (i.e., leaves, branches and layers of leaves) were scanned using a continuous-wave TLS system that employs phase-shift technology. Manual extraction of ghost points was done in order to calculate the relative amount of ghost points per scan, or ghost points ratio (gpr). The gpr values were computed in order to: (i) analyze their relationships with variables representing the above factors; and (ii) be used as a reference to evaluate the performance of filters used for extraction of ghost points. The ghost points' occurrence was modeled by fitting regression models using different predictor variables OPEN ACCESSForests 2014, 5 1566 that represent the variables under study. The obtained results indicated that reduced models with three predictors were suitable for gpr estimation in artificial leaves and in artificial branches, with a relative root mean squared error (RMSE) of 4.7% and 3.7%, respectively; while the full model with four predictors was appropriate for artificial layers of leaves, with relative RMSE of 4.5%. According to the statistical analysis, scanning distance was identified as the most important variable for modeling ghost points occurrence. Results indicated that optimized distance-based filters relative to the scanning distance have improved the outcomes in ghost points detection, in comparison to standard filtering criteria. These results suggest that more accurate characterization of forest canopy 3D structures can be achieved by removing ghost points using the new developed method.

show abstract

Section: Discussionmentioning

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Correction of Erroneous LiDAR Measurements in Artificial Forest Canopy Experimental Setups

Cifuentes

Zande

Salas

et al. 2014

Forests

View full text Add to dashboard Cite

show abstract

“…The literature is abundant in methods for the removal or marking of jump edges, either based on segmentation principles [PRLLM08,Pet02], or by exploiting the properties of a particular sensor [MDH + 08], with an interesting overall comparison given in [THA07], to name a few. From the feature estimation point of view however, a unified formulation able to remove all points in sparse density areas, including jump edges, would solve the problem better.…”

Section: Filtering Outliersmentioning

confidence: 99%

Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments

2010

View full text Add to dashboard Cite

ENVIRONMENT models serve as important resources for an autonomous robot by providing it with the necessary task-relevant information about its habitat. Their use enables robots to perform their tasks more reliably, flexibly, and efficiently. As autonomous robotic platforms get more sophisticated manipulation capabilities, they also need more expressive and comprehensive environment models: for manipulation purposes their models have to include the objects present in the world, together with their position, form, and other aspects, as well as an interpretation of these objects with respect to the robot tasks.This thesis proposes Semantic 3D Object Models as a novel representation of the robot's operating environment that satisfies these requirements and shows how these models can be automatically acquired from dense 3D range data. The thesis contributes in two important ways to the research area acquisition of environment models.The first contribution is a novel framework for Semantic 3D Object Model acquisition from Point Cloud Data. The functionality of this framework includes robust alignment and integration mechanisms for partial data views, fast segmentation into regions based on local surface characteristics, and reliable object detection, categorization, and reconstruction. The computed models are semantic in that they infer structures in the data that are meaningful with respect to the robot task. Examples of such objects are doors and handles, supporting planes, cupboards, walls, or movable smaller objects. The second key contribution is point cloud representations based on 3D point feature histograms (3D-PFHs), which model the local surface geometry for each point. 3D-PFHs distinguish themselves from alternative 3D feature representations in that they are very fast to compute, robust against variations in pose and sampling density, and cope well with noisy sensor data. Their use substantially improves the quality of the Semantic 3D Object Models acquired, as well as the speed with which they are computed. 3D-PFHs come with specific software tools that allow for the learning of surface characteristics based on their underlying geometry, the assembly of most distinctive 3D points from a given cloud, as well as limited view-invariant correspondence search for 3D registration. IIIThe contributions presented in this thesis have been fully implemented and empirically evaluated on different robots performing different tasks in different environments. The first demonstration relates to the problem of cleaning tables by disposing the objects on them into a garbage bin with a personal robotic assistant in the presence of humans in its working space. The framework for Semantic 3D Object Model acquisition is demonstrated and used to construct dynamic 3D collision maps, annotate the surrounding world with semantic labels, and extract object clusters supported by tables in real-time performance. The second demonstration presents an on-the-fly model acquisition system for door and handle identification from n...

show abstract

“…The end points of TIN edges were classified as boundary points, if a length edge exceeded a specified threshold (as done by Tang et al 2007). …”

Section: Related Workmentioning

confidence: 99%

Flying Voxel Method with Delaunay Triangulation Criterion for Façade/Feature Detection for Computation

Truong-Hong

Laefer

Hinks

et al. 2012

J. Comput. Civ. Eng.

View full text Add to dashboard Cite

ABSTRACT:In an effort to reconstruct geometric models of building façades from terrestrial laser scanning data directly without either manual intervention or any third party computer-aid design package, a new algorithm is introduced. The algorithm detects building boundaries and features and converts the point cloud data into a solid model appropriate for computational modeling. The algorithm combines a voxel-based technique with a Delaunay triangulation based criterion. In the first phase, the algorithm detects façade boundary points from raw data. The algorithm's second phase creates a solid model using voxels in a quadtree representation.Finally, the algorithm determines whether holes are actual openings or data deficits caused by occlusions and then fills unrealistic openings. The algorithm was applied to the façades of three masonry buildings. For these buildings, the algorithm successfully detected all openings and reconstructs the façade details correctly. Geometric validation of the models against measured drawings showed overall dimensions correct to 1.2%, mostly opening areas to 3%, and simulation results within 5% of those predicted by a CAD-based model.

show abstract

A Comparative Analysis of Depth-Discontinuity and Mixed-Pixel Detection Algorithms

Abstract: Abstract

Cited by 38 publications

References 9 publications

Correction of Erroneous LiDAR Measurements in Artificial Forest Canopy Experimental Setups

Correction of Erroneous LiDAR Measurements in Artificial Forest Canopy Experimental Setups

Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments

Flying Voxel Method with Delaunay Triangulation Criterion for Façade/Feature Detection for Computation

Contact Info

Product

Resources

About