3D Object Reconstruction
61 papers with code • 4 benchmarks • 7 datasets
Image: Choy et al
Benchmarks
These leaderboards are used to track progress in 3D Object Reconstruction
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Libraries
Use these libraries to find 3D Object Reconstruction models and implementationsDatasets
ShapeNet
BEHAVE
X3D
Parcel3D
A collection of 131 CT datasets of pieces of modeling clay containing stones
Corn Seeds Dataset
Most implemented papers
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).
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images
nywang16/Pixel2Mesh
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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
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
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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
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
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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.
Neural 3D Mesh Renderer
monniert/unicorn
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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.
Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation
EdwardSmith1884/3D-Object-Super-Resolution
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NeurIPS 2018
We consider the problem of scaling deep generative shape models to high-resolution.
Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers
visinf/projects-2018-matryoshka
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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
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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.
