3D Object Reconstruction

61 papers with code • 4 benchmarks • 7 datasets

Image: Choy et al


Use these libraries to find 3D Object Reconstruction models and implementations
2 papers

Most implemented papers

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

chrischoy/3D-R2N2 2 Apr 2016

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).

Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images

nywang16/Pixel2Mesh ECCV 2018

We propose an end-to-end deep learning architecture that produces a 3D shape in triangular mesh from a single color image.

Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images

hzxie/Pix2Vox ICCV 2019

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.

A Point Set Generation Network for 3D Object Reconstruction from a Single Image

fanhqme/PointSetGeneration CVPR 2017

Our final solution is a conditional shape sampler, capable of predicting multiple plausible 3D point clouds from an input image.

3D Object Reconstruction from Hand-Object Interactions

dimtziwnas/InHandScanningICCV15_Reconstruction ICCV 2015

Recent advances have enabled 3d object reconstruction approaches using a single off-the-shelf RGB-D camera.

Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction

chenhsuanlin/3D-point-cloud-generation 21 Jun 2017

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.

Neural 3D Mesh Renderer

monniert/unicorn CVPR 2018

Using this renderer, we perform single-image 3D mesh reconstruction with silhouette image supervision and our system outperforms the existing voxel-based approach.

Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers

visinf/projects-2018-matryoshka CVPR 2018

We scale this baseline to higher resolutions by proposing a memory-efficient shape encoding, which recursively decomposes a 3D shape into nested shape layers, similar to the pieces of a Matryoshka doll.

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

hzxie/Pix2Vox 22 Jun 2020

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.