3D Object Reconstruction
State-of-the-art leaderboards
| Trend | Dataset | Best Method | Paper title | Paper | Code | Compare |
|---|---|---|---|---|---|---|
|
Greatest papers with code
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
Inspired by the recent success of methods that employ shape priors to achieve robust 3D reconstructions, we propose a novel recurrent neural network architecture that we call the 3D Recurrent Reconstruction Neural Network (3D-R2N2).
#4 best model for
3D Object Reconstruction
on Data3D−R2N2
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images
ECCV 2018
• nywang16/Pixel2Mesh
• 
We propose an end-to-end deep learning architecture that produces a 3D shape in triangular mesh from a single color image.
#2 best model for
3D Object Reconstruction
on Data3D−R2N2
Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction
• chenhsuanlin/3D-point-cloud-generation
•
Conventional methods of 3D object generative modeling learn volumetric predictions using deep networks with 3D convolutional operations, which are direct analogies to classical 2D ones.
Photometric Mesh Optimization for Video-Aligned 3D Object Reconstruction
CVPR 2019
• chenhsuanlin/photometric-mesh-optim
• 
In this paper, we address the problem of 3D object mesh reconstruction from RGB videos.
3D Object Reconstruction from a Single Depth View with Adversarial Learning
In this paper, we propose a novel 3D-RecGAN approach, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks.
Dense 3D Object Reconstruction from a Single Depth View
• Yang7879/3D-RecGAN-extended
•
Unlike existing work which typically requires multiple views of the same object or class labels to recover the full 3D geometry, the proposed 3D-RecGAN++ only takes the voxel grid representation of a depth view of the object as input, and is able to generate the complete 3D occupancy grid with a high resolution of 256^3 by recovering the occluded/missing regions.
Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation
NeurIPS 2018 • EdwardSmith1884/3D-Object-Super-Resolution
We consider the problem of scaling deep generative shape models to high-resolution.
3D OBJECT RECONSTRUCTION 3D OBJECT SUPER-RESOLUTION DEPTH ESTIMATION
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.
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
GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects
• EdwardSmith1884/GEOMetrics
•
Mesh models are a promising approach for encoding the structure of 3D objects.
Learning Free-Form Deformations for 3D Object Reconstruction
Representing 3D shape in deep learning frameworks in an accurate, efficient and compact manner still remains an open challenge.
3D OBJECT RECONSTRUCTION 3D RECONSTRUCTION 3D SEMANTIC SEGMENTATION SEMANTIC SEGMENTATION