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Greatest papers with code
Learning elementary structures for 3D shape generation and matching
NeurIPS 2019
• ThibaultGROUEIX/3D-CODED
• 
We propose to represent shapes as the deformation and combination of learnable elementary 3D structures, which are primitives resulting from training over a collection of shape.
3D Point Capsule Networks
CVPR 2019
• yongheng1991/3D-point-capsule-networks
•
In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data.
Ranked #2 on
3D Object Classification
on ModelNet40
3D FEATURE MATCHING 3D GEOMETRY PERCEPTION 3D OBJECT CLASSIFICATION 3D OBJECT RECONSTRUCTION 3D PART SEGMENTATION 3D POINT CLOUD MATCHING 3D SHAPE GENERATION 3D SHAPE REPRESENTATION
StructureNet: Hierarchical Graph Networks for 3D Shape Generation
We introduce StructureNet, a hierarchical graph network which (i) can directly encode shapes represented as such n-ary graphs; (ii) can be robustly trained on large and complex shape families; and (iii) can be used to generate a great diversity of realistic structured shape geometries.
Learning to Dress 3D People in Generative Clothing
To our knowledge, this is the first generative model that directly dresses 3D human body meshes and generalizes to different poses.
3D HUMAN POSE ESTIMATION 3D HUMAN RECONSTRUCTION 3D SHAPE GENERATION 3D SHAPE MODELING
3DN: 3D Deformation Network
Given such a source 3D model and a target which can be a 2D image, 3D model, or a point cloud acquired as a depth scan, we introduce 3DN, an end-to-end network that deforms the source model to resemble the target.
Multi-chart Generative Surface Modeling
• helibenhamu/multichart3dgans
• 
The new tensor data representation is used as input to Generative Adversarial Networks for the task of 3D shape generation.
SurfNet: Generating 3D shape surfaces using deep residual networks
3D shape models are naturally parameterized using vertices and faces, \ie, composed of polygons forming a surface.
PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree Conditions
ECCV 2020
• daerduoCarey/pt2pc
•
3D generative shape modeling is a fundamental research area in computer vision and interactive computer graphics, with many real-world applications.
Learning a Hierarchical Latent-Variable Model of 3D Shapes
• lorenmt/vsl
•
We propose the Variational Shape Learner (VSL), a generative model that learns the underlying structure of voxelized 3D shapes in an unsupervised fashion.
Ranked #4 on
3D Object Recognition
on ModelNet40
3D OBJECT CLASSIFICATION 3D OBJECT RECOGNITION 3D RECONSTRUCTION 3D SHAPE GENERATION
Combinatorial 3D Shape Generation via Sequential Assembly
To alleviate this consequence induced by a huge number of feasible combinations, we propose a combinatorial 3D shape generation framework.