Recurrent Slice Networks for 3D Segmentation of Point Clouds

@article{Huang2018RecurrentSN,
  title={Recurrent Slice Networks for 3D Segmentation of Point Clouds},
  author={Qiangui Huang and Weiyue Wang and Ulrich Neumann},
  journal={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2018},
  pages={2626-2635}
}
Point clouds are an efficient data format for 3D data. However, existing 3D segmentation methods for point clouds either do not model local dependencies [21] or require added computations [14, 23]. This work presents a novel 3D segmentation framework, RSNet1, to efficiently model local structures in point clouds. The key component of the RSNet is a lightweight local dependency module. It is a combination of a novel slice pooling layer, Recurrent Neural Network (RNN) layers, and a slice… 
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Feature fusion network based on attention mechanism for 3D semantic segmentation of point clouds

PointConv: Deep Convolutional Networks on 3D Point Clouds

The dynamic filter is extended to a new convolution operation, named PointConv, which can be applied on point clouds to build deep convolutional networks and is able to achieve state-of-the-art on challenging semantic segmentation benchmarks on 3D point clouds.

Learning to Segment 3D Point Clouds in 2D Image Space

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References

SHOWING 1-10 OF 38 REFERENCES

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

This paper designs a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of points in the input and provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing.

SEGCloud: Semantic Segmentation of 3D Point Clouds

SEGCloud is presented, an end-to-end framework to obtain 3D point-level segmentation that combines the advantages of NNs, trilinear interpolation(TI) and fully connected Conditional Random Fields (FC-CRF).

Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models

A new deep learning architecture that is designed for 3D model recognition tasks and works with unstructured point clouds, Kd-networks, which demonstrates competitive performance in a number of shape recognition tasks such as shape classification, shape retrieval and shape part segmentation.

3D point cloud object detection with multi-view convolutional neural network

  • G. PangU. Neumann
  • Computer Science
    2016 23rd International Conference on Pattern Recognition (ICPR)
  • 2016
Efficient detection of three dimensional (3D) objects in point clouds is a challenging problem. Performing 3D descriptor matching or 3D scanning-window search with detector are both time-consuming…

Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks

This paper proposes a computationally efficient approach to detecting objects natively in 3D point clouds using convolutional neural networks (CNNs) by leveraging a feature-centric voting scheme to implement novel convolutionan layers which explicitly exploit the sparsity encountered in the input.

Point cloud labeling using 3D Convolutional Neural Network

  • Jing HuangSuya You
  • Computer Science
    2016 23rd International Conference on Pattern Recognition (ICPR)
  • 2016
This paper introduces a 3D point cloud labeling scheme based on 3D Convolutional Neural Network that minimizes the prior knowledge of the labeling problem and does not require a segmentation step or hand-crafted features as most previous approaches did.

Exemplar-Based 3D Shape Segmentation in Point Clouds

This paper addresses the problem of automatic 3D shape segmentation in point cloud representation by registering the target shape with exemplar shapes in a piece-wise rigid manner, so that pieces under the same rigid transformation are more likely to be in the same segment.

SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation

This paper introduces a spectral parametrization of dilated convolutional kernels and a spectral transformer network that enables weight sharing by parametrizing kernels in the spectral domain spanned by graph Laplacian eigenbases and strives to overcome two key challenges: how to share coefficients and conduct multi-scale analysis in different parts of the graph for a single shape.

Fast and Robust Multi-view 3D Object Recognition in Point Clouds

A new recognition approach is described that projects a 3D point cloud into several 2D depth images from multiple viewpoints, transforming the 3D recognition problem into a series of 2D detection problems, reducing complexity, stabilizes performance, and significantly speeds up the recognition process.

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

A hierarchical neural network that applies PointNet recursively on a nested partitioning of the input point set and proposes novel set learning layers to adaptively combine features from multiple scales to learn deep point set features efficiently and robustly.