3D Object Reconstruction Edit Task
Computer Vision • Object Reconstruction
About
Image: Choy et al
Benchmarks
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
|
||||||
|
Subtasks
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).
Ranked #4 on
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.
Ranked #3 on
3D Object Reconstruction
on Data3D−R2N2
(Avg F1 metric)
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.
Ranked #1 on
3D Object Reconstruction
on ShapeNet
A Point Set Generation Network for 3D Object Reconstruction from a Single Image
CVPR 2017
• fanhqme/PointSetGeneration
•
Our final solution is a conditional shape sampler, capable of predicting multiple plausible 3D point clouds from an input image.
Ranked #2 on
3D Reconstruction
on Data3D−R2N2
(using extra training data)
3D OBJECT RECONSTRUCTION FROM A SINGLE IMAGE 3D RECONSTRUCTION
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.
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 #3 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
Pix2Vox++: Multi-scale Context-aware 3D Object Reconstruction from Single and Multiple Images
• hzxie/Pix2Vox
•
A multi-scale context-aware fusion module is then introduced to adaptively select high-quality reconstructions for different parts from all coarse 3D volumes to obtain a fused 3D volume.
Ranked #1 on
3D Object Reconstruction
on Data3D−R2N2
Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images
Then, a context-aware fusion module is introduced to adaptively select high-quality reconstructions for each part (e. g., table legs) from different coarse 3D volumes to obtain a fused 3D volume.
Ranked #2 on
3D Object Reconstruction
on Data3D−R2N2
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.