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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 OBJECT RECONSTRUCTION

3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction

2 Apr 2016chrischoy/3D-R2N2

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).

3D OBJECT RECONSTRUCTION 3D RECONSTRUCTION

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

21 Jun 2017chenhsuanlin/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 OBJECT RECONSTRUCTION POINT CLOUD GENERATION

Pix2Vox++: Multi-scale Context-aware 3D Object Reconstruction from Single and Multiple Images

22 Jun 2020hzxie/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.

3D OBJECT RECONSTRUCTION

Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion

CVPR 2020 jchibane/if-net

To solve this, we propose Implicit Feature Networks (IF-Nets), which deliver continuous outputs, can handle multiple topologies, and complete shapes for missing or sparse input data retaining the nice properties of recent learned implicit functions, but critically they can also retain detail when it is present in the input data, and can reconstruct articulated humans.

3D OBJECT RECONSTRUCTION 3D RECONSTRUCTION 3D SHAPE RECONSTRUCTION

3D Object Reconstruction from a Single Depth View with Adversarial Learning

26 Aug 2017Yang7879/3D-RecGAN

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.

3D OBJECT RECONSTRUCTION

GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects

31 Jan 2019EdwardSmith1884/GEOMetrics

Mesh models are a promising approach for encoding the structure of 3D objects.

 Ranked #1 on 3D Object Reconstruction on Data3D−R2N2 (Avg F1 metric)

3D OBJECT RECONSTRUCTION