Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds

Engelmann, Francis; Kontogianni, Theodora; Hermans, Alexander; Leibe, Bastian

doi:10.1109/iccvw.2017.90

Cited by 253 publications

(196 citation statements)

References 33 publications

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“…Models based on KD-trees [37,94,20] spatially partition the points using kd-trees and then recursively aggregate them. RNNs [31,89,17,41] are applied to point clouds by the assumption that "order matters" [72] and achieve promising results on semantic segmentation tasks but the quality of the learned features is not clear.…”

Section: Deep Learning On Point Cloudsmentioning

confidence: 99%

Unsupervised Multi-Task Feature Learning on Point Clouds

Hassani

Haley

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

183

117

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We introduce an unsupervised multi-task model to jointly learn point and shape features on point clouds. We define three unsupervised tasks including clustering, reconstruction, and self-supervised classification to train a multi-scale graph-based encoder. We evaluate our model on shape classification and segmentation benchmarks. The results suggest that it outperforms prior state-of-the-art unsupervised models: In the ModelNet40 classification task, it achieves an accuracy of 89.1% and in ShapeNet segmentation task, it achieves an mIoU of 68.2 and accuracy of 88.6%.

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Section: Deep Learning On Point Cloudsmentioning

confidence: 99%

Unsupervised Multi-Task Feature Learning on Point Clouds

Hassani

Haley

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

183

117

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“…mean IoU Overall accuracy(%) PointNet [4] 47.71 78.62 A-SCN [33] 52.72 81.59 SEGCloud [27] 48.92 -G+RCU [8] 49.7 81.1 RSNet [12] 56.47 -Engelmann et al [9] 58 IoU of our model is 56.63% and the overall accuracy is 84.13%. Some of the experimental results are shown in Figure 6.…”

Section: Methodsmentioning

confidence: 81%

Point clouds learning with attention-based graph convolution networks

2020

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Point clouds data, as one kind of representation of 3D objects, are the most primitive output obtained by 3D sensors. Unlike 2D images, point clouds are disordered and unstructured. Hence it is not straightforward to apply classification techniques such as the convolution neural network to point clouds analysis directly. To solve this problem, we propose a novel network structure, named Attention-based Graph Convolution Networks (AGCN), to extract point clouds features. Taking the learning process as a message propagation between adjacent points, we introduce an attention mechanism to AGCN for analyzing the relationships between local features of the points. In addition, we introduce an additional global graph structure network to compensate for the relative information of the individual points in the graph structure network. The proposed network is also extended to an encoder-decoder structure for segmentation tasks. Experimental results show that the proposed network can achieve state-of-the-art performance in both classification and segmentation tasks.

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“…6. Our model demonstrates better segmentation results compared with PointNet [26], MS+CU (2) [8], G+RCU [8], 3P-RNN [42], SPGraph [15], and TangentConv [35]. However, our model performs slightly worse than PointCNN [18] due to their non-overlapping block sampling strategy with paddings which we do not use.…”

Section: Semantic Segmentation In Scenesmentioning

confidence: 92%

A-CNN: Annularly Convolutional Neural Networks on Point Clouds

Komarichev

Zhong

Hua

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

265

139

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Analyzing the geometric and semantic properties of 3D point clouds through the deep networks is still challenging due to the irregularity and sparsity of samplings of their geometric structures. This paper presents a new method to define and compute convolution directly on 3D point clouds by the proposed annular convolution. This new convolution operator can better capture the local neighborhood geometry of each point by specifying the (regular and dilated) ring-shaped structures and directions in the computation. It can adapt to the geometric variability and scalability at the signal processing level. We apply it to the developed hierarchical neural networks for object classification, part segmentation, and semantic segmentation in large-scale scenes. The extensive experiments and comparisons demonstrate that our approach outperforms the state-of-the-art methods on a variety of standard benchmark datasets (e.g., ModelNet10, ModelNet40, ShapeNetpart, S3DIS, and ScanNet).

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Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds

Cited by 253 publications

References 33 publications

Unsupervised Multi-Task Feature Learning on Point Clouds

Unsupervised Multi-Task Feature Learning on Point Clouds

Point clouds learning with attention-based graph convolution networks

A-CNN: Annularly Convolutional Neural Networks on Point Clouds

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