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
Learning Free-Form Deformations for 3D Object Reconstruction
Representing 3D shape in deep learning frameworks in an accurate, efficient and compact manner still remains an open challenge.
Robust Attentional Aggregation of Deep Feature Sets for Multi-view 3D Reconstruction
Yang7879/AttSets
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However, GRU based approaches are unable to consistently estimate 3D shapes given different permutations of the same set of input images as the recurrent unit is permutation variant.
Deep Single-View 3D Object Reconstruction with Visual Hull Embedding
qweas120/PSVH-3d-reconstruction
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The key idea of our method is to leverage object mask and pose estimation from CNNs to assist the 3D shape learning by constructing a probabilistic single-view visual hull inside of the network.
GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects
EdwardSmith1884/GEOMetrics
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Mesh models are a promising approach for encoding the structure of 3D objects.
Photometric Mesh Optimization for Video-Aligned 3D Object Reconstruction
chenhsuanlin/photometric-mesh-optim
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CVPR 2019
In this paper, we address the problem of 3D object mesh reconstruction from RGB videos.
PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization
shunsukesaito/PIFu
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ICCV 2019
We introduce Pixel-aligned Implicit Function (PIFu), a highly effective implicit representation that locally aligns pixels of 2D images with the global context of their corresponding 3D object.
Toward 3D Object Reconstruction from Stereo Images
hzxie/Stereo-3D-Reconstruction
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Inferring the 3D shape of an object from an RGB image has shown impressive results, however, existing methods rely primarily on recognizing the most similar 3D model from the training set to solve the problem.
Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion
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.
Deformation-Aware 3D Model Embedding and Retrieval
mikacuy/deformation_aware_embedding
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ECCV 2020
We introduce a new problem of retrieving 3D models that are deformable to a given query shape and present a novel deep deformation-aware embedding to solve this retrieval task.
Learning Pose-invariant 3D Object Reconstruction from Single-view Images
Learning to reconstruct 3D shapes using 2D images is an active research topic, with benefits of not requiring expensive 3D data.
