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The idea of this list is to collect shared data and algorithms around 3D Morphable Models. You are invited to contribute to this list by adding a pull request. The original list arised from the Dagstuhl seminar on 3D Morphable Models https://www.dagstuhl.de/19102 in March 2019.

Stars: ✭ 375 (+257.14%)

Mutual labels: 3d-reconstruction, 3d-models

Tf flame

Tensorflow framework for the FLAME 3D head model. The code demonstrates how to sample 3D heads from the model, fit the model to 2D or 3D keypoints, and how to generate textured head meshes from Images.

Stars: ✭ 193 (+83.81%)

Mutual labels: 3d-reconstruction, 3d-models

Tailornet

Code for our CVPR 2020 (ORAL) paper - TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style.

Stars: ✭ 180 (+71.43%)

Mutual labels: 3d-reconstruction, 3d-models

Flame pytorch

This is a implementation of the 3D FLAME model in PyTorch

Stars: ✭ 153 (+45.71%)

Mutual labels: 3d-reconstruction, 3d-models

Point Cloud Filter

Scripts showcasing filtering techniques applied to point cloud data.

Stars: ✭ 34 (-67.62%)

Mutual labels: point-cloud, voxels

Silhouette-Guided-3D

PyTorch Code of our WACV2020 paper: Silhouette Guided Point Cloud Reconstruction beyond Occlusion

Stars: ✭ 36 (-65.71%)

Mutual labels: point-cloud, 3d-reconstruction

3d Machine Learning

A resource repository for 3D machine learning

Stars: ✭ 7,405 (+6952.38%)

Mutual labels: point-cloud, 3d-reconstruction

3d Tiles

Specification for streaming massive heterogeneous 3D geospatial datasets 🌎

Stars: ✭ 1,054 (+903.81%)

Mutual labels: point-cloud, 3d-models

Igr

Implicit Geometric Regularization for Learning Shapes

Stars: ✭ 90 (-14.29%)

Mutual labels: 3d-reconstruction

Laser Camera Calibration Toolbox

A Laser-Camera Calibration Toolbox extending from that at http://www.cs.cmu.edu/~ranjith/lcct.html

Stars: ✭ 99 (-5.71%)

Mutual labels: point-cloud

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A curated list of resources for Deep Geometry Learning

Image-based methods

2012-NIPS - Convolutional-recursive deep learning for 3d object classification. [Paper]
2014-NIPS - Depth map prediction from a single image using a multi-scale deep network. [Paper]
2014-ECCV - Learning Rich Features from RGB-D Images for Object Detection and Segmentation. [Paper]
2015-CVPR - Aligning 3D models to RGB-D images of cluttered scenes. [Paper]
2015-ICCV - Multi-view convolutional neural networks for 3d shape recognition. [Paper]
2016-CVPR - Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images. [Paper]
2016-CVPR - Volumetric and multi-view cnns for object classification on 3d data. [Paper]
2020-CVPR - Autolabeling 3D Objects with Differentiable Rendering of SDF Shape Priors. [Paper][Code]

Voxel-based methods

Dense Voxels

2015-CVPR - 3d shapenets: A deep representation for volumetric shapes. [Paper]
2015-ICCV - Multi-view convolutional neural networks for 3d shape recognition. [Paper]
2015-IROS - Voxnet: A 3d convolutional neural network for real-time object recognition. [Paper]
2015-IROS - VoxNet: A 3D convolutional neural network for realtime object recognition. [Paper]
2016-CVPR - Volumetric and multi-view cnns for object classification on 3d data. [Paper]
2016-ECCV - Vconv-DAE: Deep volumetric shape learning without object labels. [Paper]
2016-ECCV - Learning a predictable and generative vector representation for objects. [Paper]
2016-ECCV - 3d-r2n2: A unified approach for single and multi-view 3d object reconstruction. [Paper]
2016-NIPS - Learning a probabilistic latent space of object shapes via 3D generative adversarial modeling. [Paper][Code]
2017-BMVC - Orientation-boosted voxel nets for 3d object recognition. [Paper]
2020 - UCLID-Net: Single View Reconstruction in Object Space. [Paper]
2020-ECCV - CoReNet: Coherent 3D Scene Reconstruction from a Single RGB Image. [Paper]
2020-ACCV - DeepVoxels++: Enhancing the Fidelity of Novel View Synthesis from 3D Voxel Embeddings. [Paper]

Sparse Voxels

2016-NIPS - Fpnn: Field probing neural networks for 3d data. [Paper]
2017-3DV - Hierarchical surface prediction for 3d object reconstruction. [Paper]
2017-CVPR - Octnet: Learning deep 3d representations at high resolutions. [Paper]
2017-ICCV - Octree generating networks: Efficient convolutional architectures for high-resolution 3d outputs. [Paper]
2017-TOG - O-cnn: Octree-based convolutional neural networks for 3d shape analysis. [Paper]
2018-TOG - Adaptive O-CNN: A patch-based deep representation of 3D shapes. [Paper]
2020-Arxiv - Neural Sparse Voxel Fields. [Paper]
2020-TPAMI - Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis. [Paper][Code]

Surface-based representation

Point-based

2017-CVPR - A point set generation network for 3d object reconstruction from a single image. [Paper]
2017-CVPR - Pointnet: Deep learning on point sets for 3d classification and segmentation. [Paper]
2017-ICCV - Escape from cells: Deep kd-networks for the recognition of 3D point cloud models. [Paper]
2017-NIPS - PointNet++: Deep hierarchical feature learning on point sets in a metric space. [Paper]
2018-CVPR - Foldingnet: Point cloud auto-encoder via deep grid deformation. [Paper]
2018-CVPR - Point cloud upsampling network. [Paper]
2018-CVPR - Sparse lattice networks for point cloud processing. [Paper]
2018-CVPR - Tangent convolutions for dense prediction in 3d. [Paper] [Tf-Code]
2018-ICML - Learning Representations and Generative Models For 3D Point Clouds. [Paper] [Tf-Code]
2018 - Point Cloud GAN. [Paper] [Py-Tf-Hybrid-Code]
2018-TOG - Point convolutional neural networks by extension operators. [Paper]
2018-EuroGraph - PCPNet: Learning Local Shape Properties from Raw Point Clouds. [Paper][Code]
2019-WACV - High Fidelity Semantic Shape Completion for Point Clouds using Latent Optimization. [Paper]
2019-CVPR - Patch-based progressive 3D point set upsampling. [Paper]
2019-CVPR - PU-GAN: a point cloud upsampling adversarial network. [Paper]
2019-CVPR - PointConv: Deep Convolutional Networks on 3D Point Clouds. [Paper][Code]
2019-CVPR - Supervised Fitting of Geometric Primitives to 3D Point Clouds. [Paper][Code]
2019-CVPR - PartNet: A Recursive Part Decomposition Network for Fine-grained and Hierarchical Shape Segmentation. [Paper][Code]
2019-CVPR - Modeling Local Geometric Structure of 3D Point Clouds using Geo-CNN. [Paper]
2019-CVPR - PointWeb: Enhancing Local Neighborhood Features for Point Cloud Processing. [Paper][Code]
2019-ICCV - DiscoNet: Shapes learning on disconnected manifolds for 3D editing. [Paper]
2019-ICCV - Interpolated Convolutional Networks for 3D Point Cloud Understanding. [Paper]
2019-NIPS - Point-Voxel CNN for Efficient 3D Deep Learning. [Paper]
2019-TOG - LOGAN: Unpaired Shape Transform in Latent Overcomplete Space. [Paper][Code]
2019 - ConvPoint: Continuous Convolutions for Point Cloud Processing. [Paper] [Py-Code]
2019-CGF - PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds. [Paper][Code]
2020-ICLR - Unpaired Point Cloud Completion on Real Scans using Adversarial Training. [Paper][Code]
2020-CVPR - AdaCoSeg: Adaptive Shape Co-Segmentation with Group Consistency Loss. [Paper]
2020-CVPR - PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. [Paper][Code]
2020-ICRA - Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. [Paper]
2020-ECCV - SoftPoolNet: Shape Descriptor for Point Cloud Completion and Classification. [Paper]
2020-ECCV - Discrete Point Flow Networks for Efficient Point Cloud Generation. [Paper]
2020-ECCV - Learning Gradient Fields for Shape Generation. [Paper][Code]
2020-ECCV - Intrinsic Point Cloud Interpolation via Dual Latent Space Navigation. [Paper][Code]
2020-ECCV - Quaternion Equivariant Capsule Networks for 3D Point Clouds. [Paper]
2020-ECCV - SoftPoolNet: Shape Descriptor for Point Cloud Completion and Classification. [Paper]
2020-ECCV - DeepFit: 3D Surface Fitting via Neural Network Weighted Least Squares. [Paper]
2020-ECCV - Multimodal Shape Completion via Conditional Generative Adversarial Networks. [Paper]
2020-Arxiv - Point Cloud Completion by Skip-attention Network with Hierarchical Folding. [Paper]
2020-Arxiv - Detail Preserved Point Cloud Completion via Separated Feature Aggregation. [Paper]
2020-Arxiv - Point Cloud Completion by Learning Shape Priors. [Paper]
2020-Arxiv - KAPLAN: A 3D Point Descriptor for Shape Completion. [Paper]
2020-Arxiv - Self-Supervised Learning of Point Clouds via Orientation Estimation. [Paper]
2020-Arxiv - CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations. Paper
2020-Arxiv - LPMNet: Latent Part Modification and Generation for 3D Point Clouds. [Paper]
2020-Arxiv - Weakly-supervised 3D Shape Completion in the Wild. [Papr]
2020-Arxiv - CAD-PU: A Curvature-Adaptive Deep Learning Solution for Point Set Upsampling. [Paper]
2020-Arxiv - Skeleton-bridged Point Completion: From Global Inference to Local Adjustment. [Paper]
2020-Arxiv - Hausdorff Point Convolution with Geometric Priors. [Paper]
2021-Arxiv - Cycle4Completion: Unpaired Point Cloud Completion using Cycle Transformation with Missing Region Coding. [Paper]

Mesh-based

2016-ECCV - Deep learning 3D shape surfaces using geometry images. [Paper]
2016-NIPS - Learning shape correspondence with anisotropic convolutional neural networks. [Paper]
2017-TOG - Convolutional neural networks on surfaces via seamless toric covers. [Paper]
2017-CVPR - Geometric deep learning on graphs and manifolds using mixture model cnns. [Paper]
2017-ICCV - Directionally convolutional networks for 3D shape segmentation. [Paper]
2018-CVPR - Feastnet: Feature-steered graph convolutions for 3d shape analysis. [Paper]
2018-CVPR - SplineCNN: Fast geometric deep learning with continuous b-spline kernels. [Paper]
2018-CVPR - AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation. [Paper]
2018-ECCV - Pixel2Mesh: Generating 3D mesh models from single RGB images. [Paper]
2018-Arxiv - Convolutional neural networks on 3d surfaces using parallel frames. [Paper]
2019-TOG - MeshCNN: a network with an edge. [Paper]
2019-ICCV - Pixel2Mesh++: Multi-view 3D mesh generation via deformation. [Paper]
2019-ICCVW - Learning to Reconstruct Symmetric Shapes using Planar Parameterization of 3D Surface. [Paper]
2020-ECCV - DR-KFS: A Differentiable Visual Similarity Metric for 3D Shape Reconstruction. [Paper]
2020-ECCV - Weakly-supervised 3D Shape Completion in the Wild. [Paper]

Implicit representation

2019-CVPR - Learning Implicit Fields for Generative Shape Modeling. [Paper] [Code]
2019-CVPR - Occupancy networks: Learning 3D reconstruction in function space. [Paper] [Code]
2019-CVPR - DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation. [Paper] [Code]
2019-ICCV - PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization. [Paper]
2019-ICCV - Learning Shape Templates with Structured Implicit Functions. [Paper]
2019-NIPS - Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations. [Paper] (implicity not verified)
2019-NIPS - DISN: Deep implicit surface network for high-quality single-view 3D reconstruction. [Paper] [Code]
2019-NIPS - Learning to infer implicit surfaces without 3D supervision. [Paper]
2019-Arxiv - Deep structured implicit functions. [Paper]
2019-Arxiv - Learning shape templates with structured implicit functions. [Paper]
2020-Arxiv - Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction. [Paper]
2020 - MeshSDF: Differentiable Iso-Surface Extraction. [Paper]
2020-ECCV - Combining Implicit Function Learning and Parametric Models for 3D Human Reconstruction. [Paper][Code]
2020-ECCV - Ladybird: Quasi-Monte Carlo Sampling for Deep Implicit Field Based 3D Reconstruction with Symmetry. [Paper]
2020-ECCV - Pix2Surf: Learning Parametric 3D Surface Models of Objects from Images. [Paper]
2020-ECCV - Points2Surf: Learning Implicit Surfaces from Point Cloud Patches. [Paper][Code]
2020-ECCV - Curriculum DeepSDF. [Paper][Code]
2020-NIPS - Neural Unsigned Distance Fields for Implicit Function Learning. [Paper]
2020-NIPS - Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence. [Paper]
2020 - PatchNets: Patch-Based Generalizable Deep Implicit 3D Shape Representations. [Paper]
2020-Arxiv - SkeletonNet: A Topology-Preserving Solution for Learning Mesh Reconstruction of Object Surfaces from RGB Images. [Paper]
2020-Arxiv - Implicit Feature Networks for Texture Completion from Partial 3D Data. [Paper]
2020-Arxiv - Overfit Neural Networks as a Compact Shape Representation. [Paper]
2020-MM - Vaccine-style-net: Point Cloud Completion in Implici Continuous Function Space. [Paper]
2020-Arxiv - Learning Occupancy Function from Point Clouds for Surface Reconstruction. [Paper]
2020-Arxiv - DUDE: Deep Unsigned Distance Embeddings for Hi-Fidelity Representation of Complex 3D Surfaces. [Paper]
2020-Arxiv - Deep Implicit Templates for 3D Shape Representation. [Paper]
2020-Arxiv - NeuralFusion: Online Depth Fusion in Latent Space. [Paper]
2021-Arxiv - Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes. [Paper][Code]
2021-Arxiv - Secrets of 3D Implicit Object Shape Reconstruction in the Wild. [Paper]
2021-Arxiv - Generative Models as Distributions of Functions. [paper]
2021-Arxiv - Shelf-Supervised Mesh Prediction in the Wild. [Paper]
2021-Arxiv - Holistic 3D Scene Understanding from a Single Image with Implicit Representation. [Paper]

Structure/Part-based representation

2011-EG - Symmetry hierarchy of man-made objects. [Paper]
2014-TOG - Structure-aware shape processing. [Paper]
2015-CGF - Analysis and synthesis of 3d shape families via deep-learned generative models of surfaces. [Paper]
2017-CGF - The shape variational autoencoder: A deep generative model of part-segmented 3d objects. [Paper]
2018-TOG - Global-to-local generative model for 3d shapes. [Paper][Tf-Code]
2019-ICCV - Composite Shape Modeling via Latent Space Factorization. [Paper]
2019 - Learning structural graph layouts and 3d shapes for long span bridges 3d reconstruction. [Paper]
2019-ICCV - CompoNet: Learning to Generate the Unseen by Part Synthesis and Composition. [Paper][Tf-Code]
2019-ICCV - BAE-NET: Branched Autoencoder for Shape Co-Segmentation. [Paper][Tf-Code]
2019-TOG - Structurenet: Hierarchical graph networks for 3d shape generation. [Paper] [Tf-Code]
2019-TOG - SAGNet: Structure-aware Generative Network for 3D-Shape Modeling. [Paper] [Tf-Code]
2019-TOG - SDM-NET: Deep generative network for structured deformable mesh. [Paper] [Code]
2020-CVPR - BSP-Net: Generating compact meshes via binary space partitioning. [Paper][Code]
2020-CVPR - PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes. [Paper][Code]
2020-CVPR - SSRNet: Scalable 3D Surface Reconstruction Network. Paper
2020-CVPR - Neural Implicit Embedding for Point Cloud Analysis. Paper
2020-CVPR - FroDO: From Detections to 3D Objects. Paper
2020-CVPR - StructEdit: Learning Structural Shape Variations. [Paper][Code]
2020-SigAsia - ShapeAssembly: Learning to Generate Programs for 3D Shape Structure Synthesis. [Paper][Code]
2020-Arxiv - A Simple and Scalable Shape Representation for 3D Reconstruction. [Paper]
2020-Arxiv - Topology-Aware Single-Image 3D Shape Reconstruction. [Paper]
2020-Arxiv - Info3D: Representation Learning on 3D Objects using Mutual Information Maximization and Contrastive Learning. Paper
2020-Arxiv - COALESCE: Component Assembly by Learning to Synthesize Connections. Paper
2020-Arxiv - DSM-Net: Disentangled Structured Mesh Net for Controllable Generation of Fine Geometry. [Paper]
2020-ECCV - GSIR: Generalizable 3D Shape Interpretation and Reconstruction. [Paper]
2020-ECCV - Learning 3D Part Assembly from a Single Image. [Paper][Code]
2020-Arxiv - TM-NET: Deep Generative Networks for Textured Meshes. [Paper]
2021-Arxiv - Towards Generalising Neural Implicit Representations. [Paper]
2021- Extending StructureNet to Generate Physically Feasible 3D Shapes. [Paper]
2021- How to represent part-whole hierarchies in a neural network. [Paper]

Deformation-based methods

2016-TOG - Efficient and flexible deformation representation for data-driven surface modeling. [Paper]
2018-CVPR - Variational autoencoders for deforming 3d mesh models. [Paper]
2019-TVCG - Sparse data driven mesh deformation. [Paper]
2018-AAAI - Mesh-based autoencoders for localized deformation component analysis. [Paper]
2018-TOG - Automatic unpaired shape deformation transfer. [Paper]
2019-Arxiv - NASA: Neural articulated shape approximation. [Paper]
2020-ECCV - LIMP: Learning Latent Shape Representations with Metric Preservation Priors. [Paper]
2020-Siggraph - Point2Mesh: A Self-Prior for Deformable Meshes. [Paper][Code]
2020-Arxiv - Better Patch Stitching for Parametric Surface Reconstruction. [Paper]
2020-NIPS - ShapeFlow: Learnable Deformations Among 3D Shapes. [Paper]
2020-ECCV - NASA Neural Articulated Shape Approximation. [Paper]

Primitve-based methods

1971-CSSC - Visual perception by computer.
1986-TOG - Constructive solid geometry for polyhedral objects. [Paper]
2016 - Volumetric Hierarchical Approximate Convex Decomposition. [Paper]
2017-CVPR - Learning shape abstractions by assembling volumetric primitives. [Paper][Code][Py-Code]
2017-ICCV - 3dprnn: Generating shape primitives with recurrent neural networks. [Paper][Code]
2017-TOG - GRASS: Generative recursive autoencoders for shape structures. [Paper][Code][Py-Code]
2017-TOG - ComplementMe: Weakly-Supervised Component Suggestions for 3D Modeling. [Paper][Code]
2018-CVPR - Csgnet: Neural shape parser for constructive solid geometry. [Paper][Code]
2018-CVPR - Im2struct: Recovering 3d shape structure from a single RGB image. [Paper][Code]
2018-ECCV - Physical Primitive Decomposition. [Paper]
2018-NIPS - Learning to Infer Graphics Programs from Hand-Drawn Images. [Paper][Code]
2018-SigAsia - FrankenGAN: Guided Detail Synthesis for Building Mass Models Using Style-Synchonized GANs. [Paper][Code]
2018-TOG - InverseCSG: Automatic Conversion of 3D Models to CSG Trees. [Paper]
2018-3DV - Parsing Geometry Using Structure-Aware Shape Templates. [Paper][Code]
2019-CVPR - Supervised fitting of geometric primitives to 3d point clouds. [Paper][Code]
2019-CVPR - Superquadrics Revisited: Learning 3D Shape Parsing Beyond Cuboids. [Paper][Code]
2019-CVPR - Unsupervised Primitive Discovery for Improved 3D Generative Modeling. [Paper]
2019-ICCV - Learning Shape Templates with Structured Implicit Functions. [Paper][Code]
2019-NIPS - Learning elementary structures for 3d shape generation and matching. [Paper][Code]
2019-ICLR - Learning to Infer and Execute 3D Shape Programs. [Paper][Code]
2019-IJCV - Learning Single-Image 3D Reconstruction by Generative Modelling of Shape, Pose and Shading. [Paper][Code]
2019-IGARSS - Primitive-Based 3D Building Modeling, Sensor Simulation, and Estimation. [Paper]
2019-TOG - Learning Adaptive Hierarchical Cuboid Abstractions of 3D Shape Collections. [Paper][Code]
2019-TPAMI - PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models. [Paper]
2020-CVPR - Learning Unsupervised Hierarchical Part Decomposition of 3D Objects from a Single RGB Image. [Paper][Code]
2020-CVPR - DualSDF: Semantic Shape Manipulation using a Two-Level Representation. [Paper][Code]
2020-CVPR - CvxNet: Learnable Convex Decomposition. [Paper][Code]
2020-CVPR - Local Deep Implicit Functions for 3D Shape. [Paper][Code]
2020-CVPR - Deep Parametric Shape Predictions using Distance Fields. [Paper][Code]
2020-CVPR - Learning Generative Models of Shape Handles. [Paper]
2020-ECCV - ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds. [Paper][Code]
2020-ECCV - Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions. [Paper][Code]
2020-BMVC - 3D-GMNet: Single-View 3D Shape Recovery as A Gaussian Mixture. [Paper]
2020-IWOBI - Recovery of Superquadrics from Range Images using Deep Learning: A Preliminary Study. [Paper]
2020-NIPS - Neural Star Domain as Primitive Representation. [Paper]
2020-NIPS - PIE-NET: Parametric Inference of Point Cloud Edges. [Paper][Code]
2020-NIPS - UCSG-NET - Unsupervised Discovering of Constructive Solid Geometry Tree. [Paper][Code]
2020-CGF - Learning Generative Models of 3D Structures. [Paper]
2020 - Geometric Primitives in LiDAR Point Clouds: A Review. [Paper]
2020 - Unsupervised Deep Learning for Primitive-Based Shape Abstraction. [Paper]
2020 - Dynamic Plane Convolutional Occupancy Networks. [Paper]
2020-Arxiv - Learning to Infer Shape Programs Using Latent Execution Self Training. [Paper]
2020-IEEEAccess - Learning to predict superquadric parameters from depth images with explicit and implicit supervision. [Paper]
2021 - Facilitated editing of generative design geometry in computer aided design user interface. [Paper]
2021 - Mixture of Volumetric Primitives for Efficient Neural Rendering. [Paper]
2021-Arxiv - EllipsoidNet: Ellipsoid Representation for Point Cloud Classification and Segmentation. [Paper]
2021- A deep neural network based shape unification to define a 3-Dimensional shape. [Paper]. [Paper]
2021-Arxiv - On the Complexity of the CSG Tree Extraction Problem. [Paper]

Survey Papers

2019-Arxiv - Deep learning for 3D point clouds: A survey. [Paper]
2020-Arxiv - A Survey on Deep Geometry Learning: From a Representation Perspective. [Paper]
2020-Arxiv - Image-based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era. [Paper]
2020-MTA - Single image 3D object reconstruction based on deep learning: A review. [Paper]
2020-CG - Single-View 3D reconstruction: A Survey of deep learning methods. [Paper]
2021-Arxiv - Attention Models for Point Clouds in Deep Learning: A Survey. [Paper]
2021-MTA - A survey of recent 3D scene analysis and processing methods. [Paper]
2021-Arxiv - A comprehensive survey on point cloud registration. [Paper]

Misc

Learning to Generate 3D Training Data - [Thesis]
2020-Arxiv - Deep Optimized Priors for 3D Shape Modeling and Reconstruction. [Paper]

Datasets

Part Labels

2009-TOG - A Benchmark for 3D Mesh Segmentation. [Paper][Dataset]
2010-TOG - Learning 3D Mesh Segmentation and Labeling. [Paper][Dataset]
2012-TOG - Active Co-Analysis of a Set of Shapes. [Paper][Dataset][Project Page]
2016-TOG - Point labels on ShapeNet Data - A Scalable Active Framework for Region Annotation in 3D Shape Collections. [Paper][Dataset]
2017-CVPR - Mesh labels on ShapeNet Data - 3D Shape Segmentation with Projective Convolutional Networks. [Paper][Dataset][Code]
2019-CVPR - PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding. [Paper][Dataset]

Other Resources

awesome point cloud analysis

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