A curated list of research in 3D Semantic Segmentation(Lidar-based Method).
You are very welcome to pull request to update this list. 😃
- SemanticKitti Dataset
- 3,712 training samples
- 3,769 validation samples
- 7,518 testing samples
- Waymo Dataset
- Conference on Computer Vision and Pattern Recognition(CVPR)
- International Conference on Computer Vision(ICCV)
- European Conference on Computer Vision(ECCV)
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CVPR 2019 Workshop on Autonomous Driving(nuScenes 3D detection)
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CVPR 2020 Workshop on Autonomous Driving(BDD1k 3D tracking)
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CVPR 2021 Workshop on Autonomous Driving(waymo 3D detection)
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CVPR 2022 Workshop on Autonomous Driving(waymo 3D detection)
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CVPR 2021 Workshop on 3D Scene Understanding for Vision, Graphics, and Robotics
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ICCV 2021 Workshop on Autonomous Vehicle Vision (AVVision), note
- 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
- 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
- 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
- Exploring spatial context for 3D semantic segmentation of point clouds
- Recurrent slice networks for 3D segmentation of point clouds
- SplatNet: Sparse lattice networks for point cloud processing
- LatticeNet: Fast point cloud segmentation using permutohedral lattices
- 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
- 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
- Deep projective 3D semantic segmentation
- Unstructured point cloud semantic labeling using deep segmentation networks
- 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
- 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)