• Corpus ID: 238744149

Unsupervised Representation Learning for 3D Point Cloud Data

  title={Unsupervised Representation Learning for 3D Point Cloud Data},
  author={Jincen Jiang and Xuequan Lu and Wanli Ouyang and Meili Wang},
Though a number of point cloud learning methods have been proposed to handle unordered points, most of them are supervised and require labels for training. By contrast, unsupervised learning of point cloud data has received much less attention to date. In this paper, we propose a simple yet effective approach for unsupervised point cloud learning. In particular, we identify a very useful transformation which generates a good contrastive version of an original point cloud. They make up a pair… 
3D Intracranial Aneurysm Classification and Segmentation via Unsupervised Dual-branch Learning
  • Di Shao, Xuequan Lu, Xiao Liu
  • Engineering, Computer Science
  • 2022
This work introduces an unsupervised method for the detection of intracranial aneurysms based on 3D point cloud data based on a dual-branch contrastive network with an encoder for each branch and a subsequent common projection head.


Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds
This work hypothesizes that a powerful representation of a 3D object should model the attributes that are shared between parts and the whole object, and distinguishable from other objects, and proposes to learn point cloud representation by bidirectional reasoning between the local structures at different abstraction hierarchies and the global shape without human supervision.
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.
Attentional ShapeContextNet for Point Cloud Recognition
The resulting model, called ShapeContextNet, consists of a hierarchy with modules not relying on a fixed grid while still enjoying properties similar to those in convolutional neural networks - being able to capture and propagate the object part information.
Recurrent Slice Networks for 3D Segmentation of Point Clouds
This work presents a novel 3D segmentation framework, RSNet1, to efficiently model local structures in point clouds using a combination of a novel slice pooling layer, Recurrent Neural Network layers, and a slice unpooling layer.
Dynamic Graph CNN for Learning on Point Clouds
This work proposes a new neural network module suitable for CNN-based high-level tasks on point clouds, including classification and segmentation called EdgeConv, which acts on graphs dynamically computed in each layer of the network.
Convolution in the Cloud: Learning Deformable Kernels in 3D Graph Convolution Networks for Point Cloud Analysis
3D Graph Convolution Networks (3D-GCN), which is designed to extract local 3D features from point clouds across scales, while shift and scale-invariance properties are introduced.
PointCNN: Convolution On X-Transformed Points
This work proposes to learn an Χ-transformation from the input points to simultaneously promote two causes: the first is the weighting of the input features associated with the points, and the second is the permutation of the points into a latent and potentially canonical order.
Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network
A novel deep learning model for 3D point clouds is proposed, named Point2Sequence, to learn 3D shape features by capturing fine-grained contextual information in a novel implicit way, and achieves state-of-the-art performance in shape classification and segmentation tasks.
Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling
Two new operations to improve PointNet with a more efficient exploitation of local structures are presented, one focuses on local 3D geometric structures and the other exploits local high-dimensional feature structures by recursive feature aggregation on a nearest-neighbor-graph computed from 3D positions.
Semantic Context Encoding for Accurate 3D Point Cloud Segmentation
This paper proposes a simple yet effective Point Context Encoding module to capture semantic contexts of a point cloud and adaptively highlight intermediate feature maps, and introduces a Semantic Context Encoded loss (SCE-loss) to supervise the network to learn rich semantic context features.