3D-Deep-Learning-Paper-List

• Point-based networks
• Geometric Deep Learning
• Sample
• Generation
• Segmentation
• Detection
• Consolidation
• Deformation
• Completion
• Denoise

Point-based networks

pointnets

• DeepPoint3D: Learning Discriminative Local Descriptors using Deep Metric Learning on 3D Point Clouds.(arxiv 2019)

• MortonNet: Self-Supervised Learning of Local Features in 3D Point Clouds.(arxiv 2019)

• Discrete Rotation Equivariance for Point Cloud Recognition.(ICRA 2019)

• Generalizing discrete convolutions for unstructured point clouds.(arxiv 2019)

• Deep RBFNet: Point Cloud Feature Learning using Radial Basis Functions.(2019 Technical report)

• 3D Local Features for Direct Pairwise Registration.(CVPR 2019)

• Dynamic graph cnn for learning on point clouds.(arxiv 2018)

• Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling(CVPR 2018)

• Pointwise convolutional neural networks.(CVPR 2018)

• PointCNN.(NIPS 2018)

• PointSIFT: A SIFT-like network module for 3D point cloud semantic segmentation.(arxiv 2018)

• Multiresolution tree networks for 3D point cloud processing.(ECCV 2018)

• Fully-convolutional point networks for large-scale point clouds.(ECCV 2018)

• PointNet: Deep learning on point sets for 3D classification and segmentation.(CVPR 2017)

• PointNet++: Deep hierarchical feature learning on point sets in a metric space.(NIPS 2017)

point cloud compression and representation

• Point2Sequence: Learning the shape representation of 3D point clouds with an attention-based sequence to sequence network.（AAAI 2019)

• Adaptive OCNN: A patch-based deep representation of 3D shapes(TOG 2018)

• Escape from cells: deep KdNetworks for the recognition of 3D point cloud models.(ICCV 2017)

• OctNet: Learning deep 3D representations at high resolutions.(CVPR 2017)

volumetric methods

• Shape completion using 3DEncoderPredictor CNNs and shape synthesis.(CVPR 2017)

• OctNet: Learning deep 3D representations at high resolutions.(CVPR 2017)

• Voxnet: A 3D convolutional neural network for real*time object recognition.(IROS 2015)

• 3D ShapeNets: A deep representation for volumetric shapes.(CVPR 2015)

Geometric Deep Learning

• Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs.(CVPR 2017)

• Geodesic convolutional neural networks on Riemannian manifolds.(ICCV 2015)

• Spectral networks and locally connected networks on graphs.(ICLR 2014)

Sample

• Learning to Sample.(CVPR 2019)

• Modeling Point Clouds with Self-Attention and Gumbel Subset Sampling.(CVPR 2019)

• Ecnet: an edge-aware point set consolidation network.(ECCV 2018)

• Data-driven upsampling of point clouds.(arxiv 2018)

• Pointgrow: Autoregressively learned point cloud generation with self-attention.(arxiv 2018)

• PU-Net: Point Cloud Upsampling Network.(CVPR 2018)

• Deep points consolidation.(TOG 2015)

• Edge-aware point set resampling.(TOG 2013)

Generation and Reconstruction

Auto-Encoder method

• Revealing Scenes by Inverting Structure from Motion Reconstructions.(CVPR 2019)

• FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation.(CVPR 2018)

• Learning representations and generative models for 3D point clouds.(ICML 2018)

• A point set generation network for 3D object reconstruction from a single image.(CVPR2017)

Adversarial method

• Point Cloud GAN.(ICLR 2019)

• Learning Localized Generative Models for 3D Point Clouds via Graph Convolution.(ICLR 2019)

• Learning representations and generative models for 3D point clouds.(ICML 2018)

• Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling.(NIPS 2016)

Other methods

• Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction.(AAAI 2018)

• AtlasNet: A papiermache approach to learning 3D surface generation. (CVPR2017)

Segmentation

• Point Cloud Oversegmentation with Graph-Structured Deep Metric Learning.(CVPR 2019)

• JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields(CVPR 2019)

• 3D-BEVIS: Birds-Eye-View Instance Segmentation.(2019 technical Report)

• SGPN: Similarity group proposal network for 3D point cloud instance segmentation. (CVPR 2018) Code

Detection

• Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction.(CVPR 2019)

• MVX-Net: Multimodal VoxelNet for 3D Object Detection.(ICRA 2019)

• Frustum PointNets for 3D object detection from RGB-D data.(CVPR 2018)

Denoise

• 3D Point Cloud Denoising via Deep Neural Network based Local Surface Estimation.(ICASSP 2019)

Completion

• Unpaired Point Cloud Completion on Real Scans using Adversarial Training.(arxiv 2019)

Other Application

• PointNetLK: Robust & Efficient Point Cloud Registration using PointNet.(CVPR 2019)

• The Perfect Match: 3D Point Cloud Matching with Smoothed Densities.(CVPR 2019)

• Embodied Question Answering in Photorealistic Environments with Point Cloud Perception.(CVPR 2019)

• SDRSAC: Semidefinite*Based Randomized Approach for Robust Point Cloud Registration without Correspondences.(CVPR 2019)

• Weighted Point Cloud Augmentation for Neural Network Training Data Class*Imblance.(ISRPS 2019)

• Supervised Fitting of Geometric Primitives to 3D Point Clouds.(CVPR 2019 oral)

• Revealing Scenes by Inverting Structure from Motion Reconstructions.(CVPR 2019)

• DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds.(CVPR 2019 oral)(Unsupervised Learning)

• USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds.(arxiv 2019)

• PPF-FoldNet: Unsupervised learning of rotation invariant 3D local descriptors(ECCV 2018)