SEGCloud: Semantic Segmentation of 3D Point Clouds

Tchapmi, Lyne P.; Choy, Christopher; Armeni, Iro; Gwak, JunYoung; Savarese, Silvio

doi:10.1109/3dv.2017.00067

Cited by 623 publications

(382 citation statements)

References 45 publications

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“…Previous work on 3D semantic segmentation has represented LiDAR data in multiple ways: as a point cloud [3,4,22,28], a voxelized 3D space [9,24,26,34], and a spherical image [29]. The accuracy and efficiency of a method depends on its representation of the data.…”

Section: Semantic Segmentationmentioning

confidence: 99%

Sensor Fusion for Joint 3D Object Detection and Semantic Segmentation

Meyer

Charland

Hegde

et al. 2019

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

122

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In this paper, we present an extension to LaserNet, an efficient and state-of-the-art LiDAR based 3D object detector. We propose a method for fusing image data with the LiDAR data and show that this sensor fusion method improves the detection performance of the model especially at long ranges. The addition of image data is straightforward and does not require image labels. Furthermore, we expand the capabilities of the model to perform 3D semantic segmentation in addition to 3D object detection. On a large benchmark dataset, we demonstrate our approach achieves state-of-the-art performance on both object detection and semantic segmentation while maintaining a low runtime.

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Section: Semantic Segmentationmentioning

confidence: 99%

Sensor Fusion for Joint 3D Object Detection and Semantic Segmentation

Meyer

Charland

Hegde

et al. 2019

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

122

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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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“…The network consists of two convolutional layers, two max pooling layers and a fully connected layer. [77] demonstrated semantic segmentation could be refined using 3D-CNNs, tri-linear interpolation and 3D fully connected CRFs. Coarse voxel predictions are extracted from a 3D-FCNN made up of three residual layers sandwiched by two convolutional layers and max pooling throughout.…”

Section: Volumetric Approachesmentioning

confidence: 99%

A Review on Deep Learning Techniques for 3D Sensed Data Classification

2019

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Over the past decade deep learning has driven progress in 2D image understanding. Despite these advancements, techniques for automatic 3D sensed data understanding, such as point clouds, is comparatively immature. However, with a range of important applications from indoor robotics navigation to national scale remote sensing there is a high demand for algorithms that can learn to automatically understand and classify 3D sensed data. In this paper we review the current state-of-theart deep learning architectures for processing unstructured Euclidean data. We begin by addressing the background concepts and traditional methodologies. We review the current main approaches including; RGB-D, multi-view, volumetric and fully end-to-end architecture designs. Datasets for each category are documented and explained. Finally, we give a detailed discussion about the future of deep learning for 3D sensed data, using literature to justify the areas where future research would be most valuable.

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SEGCloud: Semantic Segmentation of 3D Point Clouds

Cited by 623 publications

References 45 publications

Sensor Fusion for Joint 3D Object Detection and Semantic Segmentation

Sensor Fusion for Joint 3D Object Detection and Semantic Segmentation

Point clouds learning with attention-based graph convolution networks

A Review on Deep Learning Techniques for 3D Sensed Data Classification

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