Object Classification via Planar Abstraction

Oesau, Sven; Lafarge, Florent; Alliez, Pierre

doi:10.5194/isprs-annals-iii-3-225-2016

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…In (Rusu et al, 2007) the authors fit sampled parametric models to the data for object recognition. Similarly, (Oesau et al, 2016) investigates supervised machine learning techniques to represent small indoor datasets with planar models for object recognition.…”

Section: Related Workmentioning

confidence: 99%

Classification of Aerial Photogrammetric 3d Point Clouds

Becker

Häni

Rosinskaya

et al. 2017

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

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ABSTRACT:We present a powerful method to extract per-point semantic class labels from aerial photogrammetry data. Labelling this kind of data is important for tasks such as environmental modelling, object classification and scene understanding. Unlike previous point cloud classification methods that rely exclusively on geometric features, we show that incorporating color information yields a significant increase in accuracy in detecting semantic classes. We test our classification method on three real-world photogrammetry datasets that were generated with Pix4Dmapper Pro, and with varying point densities. We show that off-the-shelf machine learning techniques coupled with our new features allow us to train highly accurate classifiers that generalize well to unseen data, processing point clouds containing 10 million points in less than 3 minutes on a desktop computer.

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Section: Related Workmentioning

confidence: 99%

Classification of Aerial Photogrammetric 3d Point Clouds

Becker

Häni

Rosinskaya

et al. 2017

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

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“…Roynard et al, (2016) uses 991 features to train a Random Forest classifier. Oesau et al, (2016) transform point clouds objects to histograms via planar abstraction.…”

Section: Related Workmentioning

confidence: 99%

Transfer Learning for Indoor Object Classification: From Images to Point Clouds

Balado

Díaz-Vilariño

Verbree

et al. 2020

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

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Abstract. Indoor furniture is of great relevance to building occupants in everyday life. Furniture occupies space in the building, gives comfort, establishes order in rooms and locates services and activities. Furniture is not always static; the rooms can be reorganized according to the needs. Keeping the building models up to date with the current furniture is key to work with indoor environments. Laser scanning technology can acquire indoor environments in a fast and precise way, and recent artificial intelligence techniques can classify correctly the objects that contain. The objective of this work is to study how to minimize the use of point cloud samples in Neural Network training, tedious to label, and replace them with images obtained from online sources. For this, point clouds are converted to images by means of rotations and projections. The conversion of a 3D vector data to a 2D raster allows the use of Convolutional Neural Networks, the achievement of several images for each acquired point cloud object and the combination with images obtained from online sources, such as Google Images. The images have been distributed among the validation and testing training sets following different percentages. The results show that, although point cloud images cannot be completely dispensed within the training set, only 10% of these achieve high accuracy in the classification.

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“…Shape detection is typically used as a prior step in a large variety of vision-related tasks ranging from surface reconstruction [2,5,29,37,20] to object recognition [4,22] and data registration [7,38]. Existing algorithms typically require two user-specified parameters: (i) a fitting tolerance that specifies the maximal distance of a datum to its associated geometric shape, and (ii) a minimal shape size σ that specifies how large a group of samples must be to be considered as a geometric primitive-typically, a number of inliers when dealing with point clouds, or a minimum area for meshes.…”

Section: Introductionmentioning

confidence: 99%

Planar Shape Detection at Structural Scales

Fang

Lafarge

Desbrun

2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

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Interpreting 3D data such as point clouds or surface meshes depends heavily on the scale of observation. Yet, existing algorithms for shape detection rely on trial-anderror parameter tunings to output configurations representative of a structural scale. We present a framework to automatically extract a set of representations that capture the shape and structure of man-made objects at different key abstraction levels. A shape-collapsing process first generates a fine-to-coarse sequence of shape representations by exploiting local planarity. This sequence is then analyzed to identify significant geometric variations between successive representations through a supervised energy minimization. Our framework is flexible enough to learn how to detect both existing structural formalisms such as the CityGML Levels Of Details, and expert-specified levels of abstraction. Experiments on different input data and classes of manmade objects, as well as comparisons with existing shape detection methods, illustrate the strengths of our approach in terms of efficiency and flexibility.

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Object Classification via Planar Abstraction

Cited by 5 publications

References 25 publications

Classification of Aerial Photogrammetric 3d Point Clouds

Classification of Aerial Photogrammetric 3d Point Clouds

Transfer Learning for Indoor Object Classification: From Images to Point Clouds

Planar Shape Detection at Structural Scales

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