3D Object Reconstruction
Leaderboards
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Greatest papers with code
Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision
NeurIPS 2016
• tensorflow/models
• 
We demonstrate the ability of the model in generating 3D volume from a single 2D image with three sets of experiments: (1) learning from single-class objects; (2) learning from multi-class objects and (3) testing on novel object classes.
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).
#5 best model for
3D 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.
#3 best model for
3D Object Reconstruction
on Data3D−R2N2
Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning
ICCV 2019
• ShichenLiu/SoftRas
• 
Rendering bridges the gap between 2D vision and 3D scenes by simulating the physical process of image formation.
Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction
• chenhsuanlin/3D-point-cloud-generation
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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 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
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.
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.
#2 best model for
3D Object Reconstruction
on Data3D−R2N2
3D OBJECT RECONSTRUCTION 3D OBJECT SUPER-RESOLUTION DEPTH ESTIMATION
GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects
• EdwardSmith1884/GEOMetrics
•
Mesh models are a promising approach for encoding the structure of 3D objects.