A list of paper related to Geometry Learning.
Each item is formatted in "geometry representation, [keywords], paper, year, source"
The geometry representation is one of the depth, point cloud, manifold, mesh, sdf, tsdf, voxel, video, octree, rgb, rgb-d
depth, [reconstruction], ROSEFusion: Random Optimization for Online Dense Reconstruction under Fast Camera Motion, 2021, TOG
depth, [reconstruction, interaction], Single Depth View Based Real-Time Reconstruction of Hand-Object Interactions, 2021, TOG
depth, [reconstruction, interaction], Physical Interaction: Reconstructing Hand-object Interactions with Physics, 2022, SIGGRAPH ASIA CONFERENCE
depth, [upsampling, sequence learning], Temporal Upsampling of Depth Maps Using a Hybrid Camera, 2019, TVCG
manifold, [completion], Bounded Manifold Completion, 2021, PR
mesh, [deformation], RIMD: Efficient and Flexible Deformation Representation for Data-Driven Surface Modeling, 2016, TOG
mesh, [deformation], Rigidity Controllable As-Rigid-As-Possible Shape Deformation, 2017, GM
mesh, [deformation], SparseAE: Mesh-based Autoencoders for Localized Deformation Component Analysis, 2018, AAAI
mesh, [deformation], MeshVAE: Variational Autoencoders for Deforming 3D Mesh Models, 2018, CVPR
mesh, [deformation], Bi-harmonic Deformation Transfer with Automatic Key Point Selection, 2018, GM
mesh, [deformation], Data-Driven Weight Optimization for Real-Time Mesh Deformation, 2019, GM
mesh, [deformation], Realtime Simulation of Thin-Shell Deformable Materials using CNN-Based Mesh Embedding, 2020, RAL
mesh, [deformation], Variational Autoencoders for Localized Mesh Deformation Component Analysis, 2021, TPAMI
mesh, [deformation], ACAP: Sparse Data Driven Mesh Deformation, 2021, TVCG
mesh, [deformation], Multiscale Mesh Deformation Component Analysis with Attention-based Autoencoders, 2021, TVCG
mesh, [deformation transfer], VC-GAN (VAE CYCLE GAN): Automatic Unpaired Shape Deformation Transfer, 2018, TOG
mesh, [deformation transfer], Fully Automatic Facial Deformation Transfer, 2020, SYMMETRY
mesh, [deformation, shape editing, interpolation], Data-Driven Shape Interpolation and Morphing Editing, 2017, CGF
mesh, [deformation, shape generation], SDM-NET: Deep Generative Network for Structured Deformable Mesh, 2019, TOG
mesh, [object detection], Mesh R-CNN, 2019, ICCV
mesh, [segmentation], Automatic 3D Tooth Segmentation using Convolutional Neural Networks in Harmonic Parameter Space, 2020, GM
mesh, [shape generation, texture], TM-NET: Deep Generative Networks for Textured Meshes, 2021, TOG
mesh, [surface triangulation], Differentiable Surface Triangulation, 2021, TOG
mesh, [symmetry detection], PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models, 2021, TVCG
mesh, [vae], Mesh Variational Autoencoders with Edge Contraction Pooling, 2020, CVPRW
mesh/octree/sdf, [reconstruction], OctField: Hierarchical Implicit Functions for 3D Modeling, 2021, NIPS
octree, [backbone, deep representation], O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis, 2017, TOG
octree, [backbone, deep representation], Adaptive O-CNN: A Patch-based Deep Representation of 3D Shapes, 2018, TOG
octree, [completion], Deep Octree-based CNNs with Output-Guided Skip Connections for 3D Shape and Scene Completion, 2020, CVPRW
octree, [volumetric shape representation], Dual Octree Graph Networks for Learning Adaptive Volumetric Shape Representations, 2022, TOG
point cloud, [backbone, classification, retrieval, segmentation, normal estimation], DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing, 2019, ICCV
point cloud, [backbone, classification, segmentation], PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation, 2017, CVPR
point cloud, [backbone, classification, segmentation], PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space, 2017, NIPS
point cloud, [backbone, classification, segmentation], Interpolated Convolutional Networks for 3D Point Cloud Understanding, 2019, ICCV
point cloud, [backbone, classification, segmentation], KPConv: Flexible and Deformable Convolution for Point Clouds, 2019, ICCV
point cloud, [backbone, classification, segmentation], ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics, 2019, ICCV
point cloud, [backbone, classification, segmetation, reconstruction, interpolation], 3D Point Capsule Networks, 2019, CVPR
point cloud, [backbone, segmentation], Tangent Convolutions for Dense Prediction in 3D, 2018, CVPR
point cloud, [completion], Deformable Shape Completion with Graph Convolutional Autoencoders, 2018, CVPR
point cloud, [completion], Multi-Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds from Multiple Angles by Joint Self-Reconstruction and Half-to-Half Prediction, 2019, ICCV
point cloud, [denoising], Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning, 2019, ICCV
point cloud, [denoising], 3D Point Cloud Denoising Using Graph Laplacian Regularization of a Low Dimensional Manifold Model, 2019, TIP
point cloud, [denoising, upsampling, adversarial defense], DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds Defense, 2019, ICCV
point cloud, [hierarchical, agglomeration], Dynamic Points Agglomeration for Hierarchical Point Sets Learning, 2019, ICCV
point cloud, [keypoint detection], USIP: Unsupervised Stable Interest Point object detection from 3D Point Clouds, 2019, ICCV
point cloud, [multi-task], Unsupervised Multi-Task Feature Learning on Point Clouds, 2019, ICCV
point cloud, [multi-view stereo], Point-Based Multi-View Stereo Network, 2019, ICCV
point cloud, [normal estimation], Orienting Point Clouds with Dipole Propagation, 2021, TOG
point cloud, [object detection], Deep Hough Voting for 3D Object object detection in Point Clouds, 2019, ICCV
point cloud, [object detection], Fast Point R-CNN, 2019, ICCV
point cloud, [object detection], M3D-RPN: Monocular 3D Region Proposal Network for Object object detection, 2019, ICCV
point cloud, [object detection], RepPoints: Point Set Representation for Object Detection, 2019, ICCV
point cloud, [object detection], STD: Sparse-to-Dense 3D Object Detector for Point Cloud, 2019, ICCV
point cloud, [recognition], LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis, 2019, ICCV
point cloud, [reconstruction], Consolidation of Unorganized Point Clouds for Surface Reconstruction, 2009, TOG
point cloud, [reconstruction], TreePartNet: Neural Decomposition of Point Clouds for 3D Tree Reconstruction, 2021, TOG
point cloud, [reconstruction, implicit surface], Deep Implicit Moving Least-Squares Functions for 3D Reconstruction, 2021, CVPR
point cloud, [registration], PCRNet: Point Cloud Registration Network using PointNet Encoding, 2019, ARXIV
point cloud, [registration], Deep Closest Point: Learning Representations for Point Cloud Registration, 2019, ICCV
point cloud, [registration], DeepVCP: An End-to-End Deep Neural Network for Point Cloud Registration, 2019, ICCV
point cloud, [registration], Robust Variational Bayesian Point Set Registration, 2019, ICCV
point cloud, [registration, localization], 3D Point Cloud Registration for Localization using a Deep Neural Network Auto-Encoder, 2017, CVPR
point cloud, [reigstration], Accelerated Gravitational Point Set Alignment with Altered Physical Laws, 2019, ICCV
point cloud, [representation], Efficient Learning on Point Clouds with Basis Point Sets, 2019, ICCV
point cloud, [representation learning], Latent-Space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds, 2019, ARXIV
point cloud, [salience detection], PointCloud Saliency Maps, 2019, ICCV
point cloud, [segmentation], Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation, 2019, ICCV
point cloud, [segmentation], VV-Net: Voxel VAE Net with Group Convolutions for Point Cloud Segmentation, 2019, ICCV
point cloud, [segmentation], Semi-Supervised 3D Shape Segmentation with Multilevel Consistency and Part Substitution, 2022, CVM
point cloud, [sequence learning], PointRNN: Point Recurrent Neural Network for Moving Point Cloud Processing, 2019, ARXIV
point cloud, [sequence learning], MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences, 2019, ICCV
point cloud, [shape generation], 3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions, 2019, ICCV
point cloud, [shape generation], PointAE: Point Auto-encoder for 3D Statistical Shape and Texture Modelling, 2019, ICCV
point cloud, [shape generation], PointFlow: 3D Point Cloud shape generation with Continuous Normalizing Flows, 2019, ICCV
point cloud, [shape generation], Learning to Generate Dense Point Clouds with Textures on Multiple Categories, 2021, WACV
point cloud, [single-view reconstruction], STD-Net: Structure-preserving and Topology-adaptive Deformation Network for Single-View 3D Reconstruction, 2021, TVCG
point cloud, [unsupervised learning], Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance Discrimination, 2021, AAAI
point cloud, [upsampling], PU-Net: Point Cloud Upsampling Network, 2018, CVPR
point cloud, [upsampling], Patch-Based Progressive 3D Point Set Upsampling, 2019, CVPR
point cloud, [upsampling], PU-GAN: a Point Cloud Upsampling Adversarial Network, 2019, ICCV
point cloud, [upsampling], PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks, 2021, CVPR
rgb, [reconstruction, pose estimation], LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimation, 2020, CVPR
rgb, [reid], Viewpoint Alignment and Discriminative Parts Enhancement in 3D Space for Vehicle ReID, 2022, TOM
rgb, [sketch, generation], DeepFaceDrawing: Deep Generation of Face Images from Sketches, 2020, TOG
rgb, [sketch, generation, editing], DeepFaceEditing: Deep Face Generation and Editing with Disentangled Geometry and Appearance Control, 2021, TOG
rgb-d, [object detection, completion], Towards Part-Based Understanding of RGB-D Scans, 2021, CVPR
rgb-d, [recomposition], Scene Recomposition by Learning-based ICP, 2020, CVPR
rgb-d, [reconstruction, tracking, deformation], Neural Non-Rigid Tracking, 2020, NIPS
rgb-d, [scene flow], SF-Net: Learning Scene Flow from RGB-D Images with CNNs, 2018, BMVC
rgb/mesh, [retrieval], Single Image 3D Shape Retrieval via Cross-Modal Instance and Category Contrastive Learning, 2021, ICCV
sdf, [completion], SG-NN: Sparse Generative Neural Networks for Self-Supervised Scene Completion of RGB-D Scans, 2020, CVPR
sdf, [reconstruction], Spline Positional Encoding for Learning 3D Implicit Signed Distance Fields, 2021, IJCAI
sdf, [retrieval, alignment], End-to-End CAD Model Retrieval and 9DoF Alignment in 3D Scans, 2019, ICCV
sdf, [shape generation], sdf-StyleGAN: Implicit sdf-Based StyleGAN for 3D Shape shape generation, 2022, SGP
video, [novel view syntheis visual editing, neural rendering], Editable Free-Viewpoint Video using a Layered Neural Representation, 2021, TOG
video, [reconstruction, gaze tracking], Real-time 3D Face Reconstruction and Gaze Tracking for Virtual Reality, 2018, VR
video, [reconstruction, gaze tracking], 3D Face Reconstruction and Gaze Tracking in the HMD for Virtual Interaction, 2022, TOM
voxel, [segmentation, object detection, interpolation], Interpolation-Aware Padding for 3D Sparse Convolutional Neural Networks, 2021, ICCV