3D Reconstruction
550 papers with code • 8 benchmarks • 54 datasets
3D Reconstruction is the task of creating a 3D model or representation of an object or scene from 2D images or other data sources. The goal of 3D reconstruction is to create a virtual representation of an object or scene that can be used for a variety of purposes, such as visualization, animation, simulation, and analysis. It can be used in fields such as computer vision, robotics, and virtual reality.
Image: Gwak et al
Benchmarks
These leaderboards are used to track progress in 3D Reconstruction
Libraries
Use these libraries to find 3D Reconstruction models and implementations
ShapeNet
ScanNet
DTU
300W
ShapeNetCore
MegaDepth
ABC Dataset
ETH3D
BlendedMVS
Scan2CAD
Subtasks
Most implemented papers
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.
Superhuman Accuracy on the SNEMI3D Connectomics Challenge
For the past decade, convolutional networks have been used for 3D reconstruction of neurons from electron microscopic (EM) brain images.
MVSNet: Depth Inference for Unstructured Multi-view Stereo
We present an end-to-end deep learning architecture for depth map inference from multi-view images.
Learning Implicit Fields for Generative Shape Modeling
czq142857/implicit-decoder
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CVPR 2019
We advocate the use of implicit fields for learning generative models of shapes and introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving the visual quality of the generated shapes.
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
Facebookresearch/deepsdf
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CVPR 2019
In this work, we introduce DeepSDF, a learned continuous Signed Distance Function (SDF) representation of a class of shapes that enables high quality shape representation, interpolation and completion from partial and noisy 3D input data.
D2-Net: A Trainable CNN for Joint Detection and Description of Local Features
In this work we address the problem of finding reliable pixel-level correspondences under difficult imaging conditions.
Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching
alibaba/cascade-stereo
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CVPR 2020
The deep multi-view stereo (MVS) and stereo matching approaches generally construct 3D cost volumes to regularize and regress the output depth or disparity.
ASLFeat: Learning Local Features of Accurate Shape and Localization
lzx551402/ASLFeat
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CVPR 2020
This work focuses on mitigating two limitations in the joint learning of local feature detectors and descriptors.
Learning Accurate Dense Correspondences and When to Trust Them
PruneTruong/PDCNet
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CVPR 2021
Establishing dense correspondences between a pair of images is an important and general problem.
3D Object Reconstruction from Hand-Object Interactions
Recent advances have enabled 3d object reconstruction approaches using a single off-the-shelf RGB-D camera.
