3D Convolutional Neural Networks for landing zone detection from LiDAR

Maturana, Daniel; Scherer, Sebastian

doi:10.1109/icra.2015.7139679

Cited by 182 publications

(102 citation statements)

References 23 publications

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“…One study implemented a 3D CNN for use in airborne LiDAR to identify helicopter landing zones in real time [36]. Others have used them in conjunction with terrestrial LiDAR to map obstacles for autonomous cars [37,38].…”

Section: Introductionmentioning

confidence: 99%

The Use of Three-Dimensional Convolutional Neural Networks to Interpret LiDAR for Forest Inventory

Ayrey

Hayes

2018

Remote Sensing

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Abstract:As light detection and ranging (LiDAR) technology becomes more available, it has become common to use these datasets to generate remotely sensed forest inventories across landscapes. Traditional methods for generating these inventories employ the use of height and proportion metrics to measure LiDAR returns and relate these back to field data using predictive models. Here, we employ a three-dimensional convolutional neural network (CNN), a deep learning technique that scans the LiDAR data and automatically generates useful features for predicting forest attributes. We test the accuracy in estimating forest attributes using the three-dimensional implementations of different CNN models commonly used in the field of image recognition. Using the best performing model architecture, we compared CNN performance to models developed using traditional height metrics. The results of this comparison show that CNNs produced 12% less prediction error when estimating biomass, 6% less in estimating tree count, and 2% less when estimating the percentage of needleleaf trees. We conclude that using CNNs can be a more accurate means of interpreting LiDAR data for forest inventories compared to standard approaches.

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Section: Introductionmentioning

confidence: 99%

The Use of Three-Dimensional Convolutional Neural Networks to Interpret LiDAR for Forest Inventory

Ayrey

Hayes

2018

Remote Sensing

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“…Recently, some studies have utilized 3D CNN for learning spatio-temporal features from videos [29,30], learning 3D structures from LiDAR point clouds [31], or learning spatio-spectral presentations from hyperspectral images [32]. In general, 3D CNN is not as widely applied as 2D CNN, as the temporal dimension is usually not considered in computer vision and machine learning.…”

Section: Introductionmentioning

confidence: 99%

3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images

et al. 2018

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This study describes a novel three-dimensional (3D) convolutional neural networks (CNN) based method that automatically classifies crops from spatio-temporal remote sensing images. First, 3D kernel is designed according to the structure of multi-spectral multi-temporal remote sensing data. Secondly, the 3D CNN framework with fine-tuned parameters is designed for training 3D crop samples and learning spatio-temporal discriminative representations, with the full crop growth cycles being preserved. In addition, we introduce an active learning strategy to the CNN model to improve labelling accuracy up to a required threshold with the most efficiency. Finally, experiments are carried out to test the advantage of the 3D CNN, in comparison to the two-dimensional (2D) CNN and other conventional methods. Our experiments show that the 3D CNN is especially suitable in characterizing the dynamics of crop growth and outperformed the other mainstream methods.

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“…We used PointNet as a module to extract the initial pointwise and global shape representation mainly due to its efficiency. In general, other point-based modules, or even volumetric [15,20,24] and view-based modules [9,22] for local and global shape processing could be adapted in a similar manner within our architecture. Below we describe the main focus of our work to learn the parameters of the architecture based on part hierarchies and tag data.…”

Section: Methodsmentioning

confidence: 99%

Learning Point Embeddings from Shape Repositories for Few-Shot Segmentation

Sharma

Kalogerakis

Maji

2019

2019 International Conference on 3D Vision (3DV)

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User generated 3D shapes in online repositories contain rich information about surfaces, primitives, and their geometric relations, often arranged in a hierarchy. We present a framework for learning representations of 3D shapes that reflect the information present in this meta data and show that it leads to improved generalization for semantic segmentation tasks. Our approach is a point embedding network that generates a vectorial representation of the 3D points such that it reflects the grouping hierarchy and tag data. The main challenge is that the data is noisy and highly variable. To this end, we present a tree-aware metriclearning approach and demonstrate that such learned embeddings offer excellent transfer to semantic segmentation tasks, especially when training data is limited. Our approach reduces the relative error by 10.2% with 8 training examples, by 11.72% with 120 training examples on the ShapeNet semantic segmentation benchmark, in comparison to the network trained from scratch. By utilizing tag data the relative error is reduced by 12.8% with 8 training examples, in comparison to the network trained from scratch. These improvements come at no additional labeling cost as the meta data is freely available.

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3D Convolutional Neural Networks for landing zone detection from LiDAR

Cited by 182 publications

References 23 publications

The Use of Three-Dimensional Convolutional Neural Networks to Interpret LiDAR for Forest Inventory

The Use of Three-Dimensional Convolutional Neural Networks to Interpret LiDAR for Forest Inventory

3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images

Learning Point Embeddings from Shape Repositories for Few-Shot Segmentation

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