3D Semantic Segmentation Papers

• PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
• PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
• SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud
• Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs
• 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
• SPLATNet: Sparse Lattice Networks for Point Cloud Processing
• Learning Free-Form Deformations for 3D Object Reconstruction
• Tangent Convolutions for Dense Prediction in 3D
• SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud
• TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes
• SurfConv: Bridging 3D and 2D Convolution for RGBD Images
• Associatively Segmenting Instances and Semantics in Point Clouds
• JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields
• SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
• DeepGCNs: Can GCNs Go as Deep as CNNs?
• Deep Projective 3D Semantic Segmentation
• 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks
• KPConv: Flexible and Deformable Convolution for Point Clouds
• Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds
• SalsaNet: Fast Road and Vehicle Segmentation in LiDAR Point Clouds for Autonomous Driving
• RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
• xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation
• The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge
• LatticeNet: Fast Point Cloud Segmentation Using Permutohedral Lattices
• Robust Semantic Segmentation of Brain Tumor Regions from 3D MRIs
• Depth Based Semantic Scene Completion with Position Importance Aware Loss
• 3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation
• SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving
• Atlas: End-to-End 3D Scene Reconstruction from Posed Images
• PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation
• DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes
• SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation
• JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds
• Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images
• 3D Semantic Segmentation of Modular Furniture using rjMCMC
• RangeNet++: Fast and Accurate LiDAR Semantic Segmentation