In recent years, tremendous amount of progress is being made in the field of 3D Machine Learning, which is an interdisciplinary field that fuses computer vision, computer graphics and machine learning. This repo is derived from my study notes and will be used as a place for triaging new research papers.
I'll use the following icons to differentiate 3D representations:
- 📷 Multi-view Images
- 👾 Volumetric
- 🎲 Point Cloud
- 💎 Polygonal Mesh
- 💊 Primitive-based
To make it a collaborative project, you may add content throught pull requests or open an issue to let me know.
Stanford CS468: Machine Learning for 3D Data (Spring 2017)
MIT 6.838: Shape Analysis (Spring 2017)
Princeton COS 526: Advanced Computer Graphics (Fall 2010)
Princeton CS597: Geometric Modeling and Analysis (Fall 2003)
To see a survey of RGBD datasets, I recommend to check out Michael Firman's collection as well as the associated paper, RGBD Datasets: Past, Present and Future. Point Cloud Library also has a good dataset catalogue.
👾 3D ShapeNets: A Deep Representation for Volumetric Shapes (2015) [Paper]
👾 VoxNet: A 3D Convolutional Neural Network for Real-Time Object Recognition (2015) [Paper] [Code]
📷 Multi-view Convolutional Neural Networks for 3D Shape Recognition (2015) [Paper]
📷 DeepPano: Deep Panoramic Representation for 3-D Shape Recognition (2015) [Paper]
👾📷 FusionNet: 3D Object Classification Using Multiple Data Representations (2016) [Paper]
👾📷 Volumetric and Multi-View CNNs for Object Classification on 3D Data (2016) [Paper] [Code]
👾 Generative and Discriminative Voxel Modeling with Convolutional Neural Networks (2016) [Paper] [Code]
👾 3D GAN: Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling (2016) [Paper]
👾 Generative and Discriminative Voxel Modeling with Convolutional Neural Networks (2017) [Paper]
👾 FPNN: Field Probing Neural Networks for 3D Data (2016) [Paper]
👾 OctNet: Learning Deep 3D Representations at High Resolutions (2017) [Paper]
👾 O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis (2017) [Paper]
🎲 PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation (2017) [Paper]
🎲 PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space (2017) [Paper]
📷 Feedback Networks (2017) [Paper]
Sliding Shapes for 3D Object Detection in Depth Images (2014) [Paper]
Object Detection in 3D Scenes Using CNNs in Multi-view Images (2016) [Paper]
Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images (2016) [Paper]
DeepContext: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding (2016) [Paper]
Learning 3D Mesh Segmentation and Labeling (2010) [Paper]
Unsupervised Co-Segmentation of a Set of Shapes via Descriptor-Space Spectral Clustering (2011) [Paper]
3D Shape Segmentation with Projective Convolutional Networks (2017) [Paper]
Learning Hierarchical Shape Segmentation and Labeling from Online Repositories (2017) [Paper]
🎲 PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation (2017) [Paper]
🎲 PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space (2017) [Paper]
Parametric Morphable Model-based methods
A Morphable Model For The Synthesis Of 3D Faces (1999) [Paper][Github]
The Space of Human Body Shapes: Reconstruction and Parameterization from Range Scans (2003) [Paper]
Part-based Template Learning methods
Modeling by Example (2004) [Paper]
Model Composition from Interchangeable Components (2007) [Paper]
Data-Driven Suggestions for Creativity Support in 3D Modeling (2010) [Paper]
Photo-Inspired Model-Driven 3D Object Modeling (2011) [Paper]
Probabilistic Reasoning for Assembly-Based 3D Modeling (2011) [Paper]
A Probabilistic Model for Component-Based Shape Synthesis (2012) [Paper]
Structure Recovery by Part Assembly (2012) [Paper]
Fit and Diverse: Set Evolution for Inspiring 3D Shape Galleries (2012) [Paper]
AttribIt: Content Creation with Semantic Attributes (2013) [Paper]
Learning Part-based Templates from Large Collections of 3D Shapes (2013) [Paper]
Topology-Varying 3D Shape Creation via Structural Blending (2014) [Paper]
Estimating Image Depth using Shape Collections (2014) [Paper]
Single-View Reconstruction via Joint Analysis of Image and Shape Collections (2015) [Paper]
Interchangeable Components for Hands-On Assembly Based Modeling (2016) [Paper]
Shape Completion from a Single RGBD Image (2016) [Paper]
Deep Learning Methods
📷 Learning to Generate Chairs, Tables and Cars with Convolutional Networks (2014) [Paper]
📷 Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis (2015, NIPS) [Paper]
🎲 Analysis and synthesis of 3D shape families via deep-learned generative models of surfaces (2015) [Paper]
📷 Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis (2015) [Paper] [Code]
📷 Multi-view 3D Models from Single Images with a Convolutional Network (2016) [Paper] [Code]
📷 View Synthesis by Appearance Flow (2016) [Paper] [Code]
👾 Voxlets: Structured Prediction of Unobserved Voxels From a Single Depth Image (2016) [Paper]
👾 3D-R2N2: 3D Recurrent Reconstruction Neural Network (2016) [Paper]
👾 Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision (2016) [Paper]
👾 TL-Embedding Network: Learning a Predictable and Generative Vector Representation for Objects (2016) [Paper]
👾 3D GAN: Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling (2016) [Paper]
👾 3D Shape Induction from 2D Views of Multiple Objects (2016) [Paper]
📷 Unsupervised Learning of 3D Structure from Images (2016) [Paper]
👾 Generative and Discriminative Voxel Modeling with Convolutional Neural Networks (2016) [Paper] [Code]
📷 Multi-view Supervision for Single-view Reconstruction via Differentiable Ray Consistency (2017) [Paper]
📷 Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes with Deep Generative Networks (2017) [Paper] [Code]
👾 Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs (2017) [Paper]
👾 Hierarchical Surface Prediction for 3D Object Reconstruction (2017) [Paper]
👾 OctNetFusion: Learning Depth Fusion from Data (2017) [Paper]
🎲 A Point Set Generation Network for 3D Object Reconstruction from a Single Image (2017) [Paper]
🎲 Shape Generation using Spatially Partitioned Point Clouds (2017) [Paper]
🎲 DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image (2017) [Paper]
📷 Transformation-Grounded Image Generation Network for Novel 3D View Synthesis (2017) [Paper]
📷 Tag Disentangled Generative Adversarial Networks for Object Image Re-rendering (2017) [Paper]
📷 3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks (2017) [Paper]
👾 Interactive 3D Modeling with a Generative Adversarial Network (2017) [Paper]
📷👾 Weakly supervised 3D Reconstruction with Adversarial Constraint (2017) [Paper]
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💎 Exploring Generative 3D Shapes Using Autoencoder Networks (Autodesk 2017) [Paper]
💊 GRASS: Generative Recursive Autoencoders for Shape Structures (SIGGRAPH 2017) [Paper]
Style-Content Separation by Anisotropic Part Scales (2010) [Paper]
Design Preserving Garment Transfer (2012) [Paper]
Analogy-Driven 3D Style Transfer (2014) [Paper]
Elements of Style: Learning Perceptual Shape Style Similarity (2015) [Paper]
Functionality Preserving Shape Style Transfer (2016) [Paper]
Unsupervised Texture Transfer from Images to Model Collections (2016) [Paper]
Learning Detail Transfer based on Geometric Features (2017) [Paper]
