DANCE-NET: Density-aware convolution networks with context encoding for airborne LiDAR point cloud classification

Li, Xiang; Wang, Lingjing; Wang, Mingyang; Wen, Congcong; Fang, Yi

doi:10.1016/j.isprsjprs.2020.05.023

Cited by 50 publications

(42 citation statements)

References 51 publications

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“…To avoid overfitting, Arief [17] refined a trained PointCNN model by combing the strict pairwise penalties that are used in the CRF procedure on the unseen data. To address the challenges of uneven density distribution, Li [70] introduced a density-aware convolutional module which adds an inverse density function to reweight the convolutional kernel. Wen [15] constructed the local receptive field by selecting the directionally constrained nearest neighbors, then the orientation-aware features can be aggregated by following the order of each angular segment when formulating the receptive field.…”

Section: ) Deep Learning For the Classification Of Als Point Cloudsmentioning

confidence: 99%

A Comparison of Deep Learning Methods for Airborne Lidar Point Clouds Classification

Kähler

Pfeifer

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The success achieved by deep learning techniques in image labelling has triggered a growing interest in applying deep learning for 3D point cloud classification. To provide better insights into different deep learning architectures and their applications to ALS point cloud classification, this paper presents a comprehensive comparison among three state-of-theart deep learning networks: PointNet++, SparseCNN and KPConv, on two different ALS datasets. The performances of these three deep learning networks are compared w.r.t. classification accuracy, computation time, generalization ability as well as the sensitivity to the choices of hyper-parameters. Overall, we observed that PointNet++, SparseCNN and KPConv all outperform Random Forest on the classification results. Moreover, SparseCNN leads to a slightly better classification result compared to PointNet++ and KPConv, while requiring less computation time and memory. At the same time, it shows a better ability to generalize and is less impacted by the different choices of hyper-parameters.

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Section: ) Deep Learning For the Classification Of Als Point Cloudsmentioning

confidence: 99%

A Comparison of Deep Learning Methods for Airborne Lidar Point Clouds Classification

Kähler

Pfeifer

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Table 5 displays a comparison of the classification accuracy with the aforementioned evaluation metrics among three different methods. Compared to DANCE-Net [71], we achieve a lower OA; however, we provide improved performance in classifying tree that produce a higher accuracy.…”

Section: Comparison With Other Published Methodsmentioning

confidence: 96%

Section: Comparison With Other Published Methodsmentioning

confidence: 99%

“…After illustrating the effectiveness, we compare the proposed method with other published high-accuracy methods that have available codes using the DFC 3D dataset, including DANCE-Net [71] and GA-Conv [67]. The DANCE-Net method [71] classified the ALS data by introducing a density-aware convolution module which uses the point-wise density to reweight the learnable weights of convolution kernels and further developing a multi-scale CNN model to perform per-point semantic labeling. In GA-Conv method [67], whose strategy is similar to that of the DANCE-Net [71], approximating convolution on unevenly distributed 3D point sets with a geometry-attentional network consisting of geometry-aware convolution, dense hierarchical architecture and elevation-attention module to embed the three characteristics effectively, which can be trained in an end-to-end manner.…”

Section: Comparison With Other Published Methodsmentioning

confidence: 99%

“…The DANCE-Net method [71] classified the ALS data by introducing a density-aware convolution module which uses the point-wise density to reweight the learnable weights of convolution kernels and further developing a multi-scale CNN model to perform per-point semantic labeling. In GA-Conv method [67], whose strategy is similar to that of the DANCE-Net [71], approximating convolution on unevenly distributed 3D point sets with a geometry-attentional network consisting of geometry-aware convolution, dense hierarchical architecture and elevation-attention module to embed the three characteristics effectively, which can be trained in an end-to-end manner. Furthermore, compared to the aforementioned methods, the proposed method that also operates directly on point clouds, ranks second among three strategies, with OA value that are 5.1% higher than those of GA-Conv [67].…”

Section: Comparison With Other Published Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Hierarchical Instance Recognition of Individual Roadside Trees in Environmentally Complex Urban Areas from UAV Laser Scanning Point Clouds

Wang

Jiang

Liu

et al. 2020

IJGI

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Individual tree segmentation is essential for many applications in city management and urban ecology. Light Detection and Ranging (LiDAR) system acquires accurate point clouds in a fast and environmentally-friendly manner, which enables single tree detection. However, the large number of object categories and occlusion from nearby objects in complex environment pose great challenges in urban tree inventory, resulting in omission or commission errors. Therefore, this paper addresses these challenges and increases the accuracy of individual tree segmentation by proposing an automated method for instance recognition urban roadside trees. The proposed algorithm was implemented of unmanned aerial vehicles laser scanning (UAV-LS) data. First, an improved filtering algorithm was developed to identify ground and non-ground points. Second, we extracted tree-like objects via labeling on non-ground points using a deep learning model with a few smaller modifications. Unlike only concentrating on the global features in previous method, the proposed method revises a pointwise semantic learning network to capture both the global and local information at multiple scales, significantly avoiding the information loss in local neighborhoods and reducing useless convolutional computations. Afterwards, the semantic representation is fed into a graph-structured optimization model, which obtains globally optimal classification results by constructing a weighted indirect graph and solving the optimization problem with graph-cuts. The segmented tree points were extracted and consolidated through a series of operations, and they were finally recognized by combining graph embedding learning with a structure-aware loss function and a supervoxel-based normalized cut segmentation method. Experimental results on two public datasets demonstrated that our framework achieved better performance in terms of classification accuracy and recognition ratio of tree.

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Multi-scale learnable key-channel attention network for point cloud classification and segmentation

Zhao,

Liu,

2024

Applied Soft Computing

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DANCE-NET: Density-aware convolution networks with context encoding for airborne LiDAR point cloud classification

Cited by 50 publications

References 51 publications

A Comparison of Deep Learning Methods for Airborne Lidar Point Clouds Classification

A Comparison of Deep Learning Methods for Airborne Lidar Point Clouds Classification

Hierarchical Instance Recognition of Individual Roadside Trees in Environmentally Complex Urban Areas from UAV Laser Scanning Point Clouds

Multi-scale learnable key-channel attention network for point cloud classification and segmentation

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