点云分割学习路径

Traditinal methods

• PyramNet: Point cloud pyramid attention network and graph embedding module for classification and segmentation
• Fast semantic segmentation of 3D point clouds using a dense CRF with learned parameters
• Shape-based recognition of 3d point clouds in urban environments
• Fast semantic segmentation of 3d point clouds with strongly varying density
• Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers
• Discriminative learning of markov random fields for segmentation of 3D scan data
• Robust 3D scan point classification using associative markov networks
• Contextual classification with functional max-margin markov networks

Point-based methods

Point-wise shared MLP

• PointNet: Deep learning on point sets for 3D classification and segmentation
• PointNet++: Deep hierarchical feature learning on point sets in a metric space
• PointSIFT: A SIFT-like network module for 3D point cloud semantic segmentation
• Know what your neighbors do: 3D semantic segmentation of point clouds
• RandLA-Net: Efficient semantic segmentation of large-scale point clouds
• Modeling point clouds with self-attention and gumbel subset sampling
• LSANet: Feature learning on point sets by local spatial attention
• PyramNet: Point cloud pyramid attention network and graph embedding module for classification and segmentation

Point Convolution

• PointCNN: Convolution on x-transformed points
• A-CNN: Annularly convolutional neural networks on point clouds
• KPConv: Flexible and deformable convolution for point clouds
• Dilated point convolutions: On the receptive field of point convolutions
• PointAtrousNet: Point atrous convolution for point cloud analysis
• PointAtrousGraph: Deep hierarchical encoder-decoder with atrous convolution for point clouds
• Tangent convolutions for dense prediction in 3D
• DAR-Net: Dynamic aggregation network for semantic scene segmentation
• ShellNet: Efficient point cloud convolutional neural networks using concentric shells statistics
• Point-voxel cnn for efficient 3D deep learning

Recurrent Neural Networ

• Exploring spatial context for 3D semantic segmentation of point clouds
• Recurrent slice networks for 3D segmentation of point clouds

Lattice Convolution

• SplatNet: Sparse lattice networks for point cloud processing
• LatticeNet: Fast point cloud segmentation using permutohedral lattices

Voxel-based methods

• Sparse single sweep lidar point cloud segmentation via learning contextual shape priors from scene completion paper, code
• Point cloud labeling using 3D convolutional neural network
• Segcloud: Semantic segmentation of 3D point cloud
• Fully-convolutional point networks for large-scale point clouds
• 3DCNN-DQN-RNN: A deep reinforcement learning framework for semantic parsing of large-scale 3D point clouds
• 3D semantic segmentation with submanifold sparse convolutional networks
• Efficient convolutions for real-time semantic segmentation of 3D point clouds
• VV-Net: Voxel vaenet with group convolutions for point cloud segmentation
• VolMap: A real-time model for semantic segmentation of a LiDAR surrounding view

Image-based methods

Range view-based methods

• SqueezeSeg: Convolutional neural nets with recurrent CRF for real-time road-object segmentation from 3D LiDAR point cloud
• SqueezeSegV2: Improved model structure and unsupervised domain adaptation for road-object segmentation from a LiDAR point cloud
• SqueezeSegV3: Spatially-adaptive convolution for efficient point-cloud segmentation
• Semantic segmentation of 3D LiDAR data in dynamic scene using semi-supervised learning
• RangeNet++: Fast and accurate LiDAR semantic segmentation
• LU-Net: An efficient network for 3D LiDAR point cloud semantic segmentation based on end-to-end-learned 3D features and U-Net
• 3D-MiniNet: Learning a 2D representation from point clouds for fast and efficient 3D LiDAR semantic segmentation
• DeepTemporalSeg: Temporally consistent semantic segmentation of 3D LiDAR scans
• LiSeg: Lightweight road-object semantic segmentation in 3D LiDAR scans for autonomous driving
• PointSeg: Real-time semantic segmentation based on 3D LiDAR point cloud
• RIU-Net: Embarrassingly simple semantic segmentation of 3D LiDAR point cloud
• SalsaNet: Fast road and vehicle segmentation in LiDAR point clouds for autonomous driving
• SalsaNext: Fast,uncertainty-aware semantic segmentation of LiDAR point clouds

Multi view-based methods

• Deep projective 3D semantic segmentation
• Unstructured point cloud semantic labeling using deep segmentation networks

Graph-based Methods

• Large-scale point cloud semantic segmentation with superpoint graphs
• Graph attention convolution for point cloud semantic segmentation
• Hierarchical point-edge interaction network for point cloud semantic segmentation
• Dynamic graph CNN for learning on point clouds

Survey

• Linking Points With Labels in 3D: A Review of Point Cloud Semantic Segmentation paper
• Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey and Experimental Study paper
• A Technical Survey and Evaluation of Traditional Point Cloud Clustering Methods for LiDAR Panoptic Segmentation paper
• A survey on deep learning-based precise boundary recovery of semantic segmentation for images and point clouds [paper] (https://www.sciencedirect.com/science/article/pii/S0303243421001185)