Trees Detection From Laser Point Clouds Acquired in Dense Urban Areas by a Mobile Mapping System

Monnier, Fabrice; Vallet, Bruno; Soheilian, Bahman

doi:10.5194/isprsannals-i-3-245-2012

Cited by 67 publications

(48 citation statements)

References 7 publications

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“…Further introducing a binning based on a discrete rectangular raster (e.g. with quadratic bins of side length 0.25 m), a quantization of occurrences at certain locations (X, Y ) on the plane P yields an accumulation map M (Monnier et al, 2012). Since each 3D point is mapped into a bin, the entry M (X, Y ) of the accumulation map M reveals how many 3D points voted for the same bin.…”

Section: D Feature Extractionmentioning

confidence: 99%

Feature relevance assessment for the semantic interpretation of 3D point cloud data

Weinmann

Jutzi

Mallet³

2013

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

168

108

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ABSTRACT:The automatic analysis of large 3D point clouds represents a crucial task in photogrammetry, remote sensing and computer vision. In this paper, we propose a new methodology for the semantic interpretation of such point clouds which involves feature relevance assessment in order to reduce both processing time and memory consumption. Given a standard benchmark dataset with 1.3 million 3D points, we first extract a set of 21 geometric 3D and 2D features. Subsequently, we apply a classifier-independent ranking procedure which involves a general relevance metric in order to derive compact and robust subsets of versatile features which are generally applicable for a large variety of subsequent tasks. This metric is based on 7 different feature selection strategies and thus addresses different intrinsic properties of the given data. For the example of semantically interpreting 3D point cloud data, we demonstrate the great potential of smaller subsets consisting of only the most relevant features with 4 different state-of-the-art classifiers. The results reveal that, instead of including as many features as possible in order to compensate for lack of knowledge, a crucial task such as scene interpretation can be carried out with only few versatile features and even improved accuracy.

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Section: D Feature Extractionmentioning

confidence: 99%

Feature relevance assessment for the semantic interpretation of 3D point cloud data

Weinmann

Jutzi

Mallet³

2013

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

168

108

View full text Add to dashboard Cite

show abstract

“…Based on geometric features, further non-vegetation objects can be detected and removed. A different approach relies on the calculation of geometric descriptors for each 3D point, the projection of these descriptors onto a horizontally oriented 2D accumulation map and the consideration of a spatial filtering to derive individual tree segments [63].…”

Section: Semantic Segmentationmentioning

confidence: 99%

A Classification-Segmentation Framework for the Detection of Individual Trees in Dense MMS Point Cloud Data Acquired in Urban Areas

Weinmann

Mallet³

et al. 2017

Remote Sensing

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Abstract:In this paper, we present a novel framework for detecting individual trees in densely sampled 3D point cloud data acquired in urban areas. Given a 3D point cloud, the objective is to assign point-wise labels that are both class-aware and instance-aware, a task that is known as instance-level segmentation. To achieve this, our framework addresses two successive steps. The first step of our framework is given by the use of geometric features for a binary point-wise semantic classification with the objective of assigning semantic class labels to irregularly distributed 3D points, whereby the labels are defined as "tree points" and "other points". The second step of our framework is given by a semantic segmentation with the objective of separating individual trees within the "tree points". This is achieved by applying an efficient adaptation of the mean shift algorithm and a subsequent segment-based shape analysis relying on semantic rules to only retain plausible tree segments. We demonstrate the performance of our framework on a publicly available benchmark dataset, which has been acquired with a mobile mapping system in the city of Delft in the Netherlands. This dataset contains 10.13 M labeled 3D points among which 17.6% are labeled as "tree points". The derived results clearly reveal a semantic classification of high accuracy (up to 90.77%) and an instance-level segmentation of high plausibility, while the simplicity, applicability and efficiency of the involved methods even allow applying the complete framework on a standard laptop computer with a reasonable processing time (less than 2.5 h).

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“…A second main application area is the semantic classification of point clouds acquired with mobile mapping systems, sometimes in combination with airborne LiDAR (Kim and Medioni, 2011); to extract for example roads (Boyko and Funkhouser, 2011), buildings (Pu et al, 2011), street furniture (Golovinskiy et al, 2009) or trees (Monnier et al, 2012). Several authors solve for all relevant object classes at once, like we do here, by attaching a label to every single 3D point (Weinmann et al, 2013, Dohan et al, 2015.…”

Section: Related Workmentioning

confidence: 99%

Fast Semantic Segmentation of 3d Point Clouds With Strongly Varying Density

Häckel¹,

Wegner²,

Schindler³

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

172

112

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ABSTRACT:We describe an effective and efficient method for point-wise semantic classification of 3D point clouds. The method can handle unstructured and inhomogeneous point clouds such as those derived from static terrestrial LiDAR or photogammetric reconstruction; and it is computationally efficient, making it possible to process point clouds with many millions of points in a matter of minutes. The key issue, both to cope with strong variations in point density and to bring down computation time, turns out to be careful handling of neighborhood relations. By choosing appropriate definitions of a point's (multi-scale) neighborhood, we obtain a feature set that is both expressive and fast to compute. We evaluate our classification method both on benchmark data from a mobile mapping platform and on a variety of large, terrestrial laser scans with greatly varying point density. The proposed feature set outperforms the state of the art with respect to per-point classification accuracy, while at the same time being much faster to compute.

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Trees Detection From Laser Point Clouds Acquired in Dense Urban Areas by a Mobile Mapping System

Cited by 67 publications

References 7 publications

Feature relevance assessment for the semantic interpretation of 3D point cloud data

Feature relevance assessment for the semantic interpretation of 3D point cloud data

A Classification-Segmentation Framework for the Detection of Individual Trees in Dense MMS Point Cloud Data Acquired in Urban Areas

Fast Semantic Segmentation of 3d Point Clouds With Strongly Varying Density

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