3D Reconstruction
452 papers with code • 8 benchmarks • 52 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
300W
DTU
ShapeNetCore
MegaDepth
ABC Dataset
ETH3D
BlendedMVS
Scan2CAD
Subtasks
Most implemented papers
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
Neural graphics primitives, parameterized by fully connected neural networks, can be costly to train and evaluate.
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).
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
MIT-SPARK/Kimera
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We provide an open-source C++ library for real-time metric-semantic visual-inertial Simultaneous Localization And Mapping (SLAM).
The Double Sphere Camera Model
We evaluate the model using a calibration dataset with several different lenses and compare the models using the metrics that are relevant for Visual Odometry, i. e., reprojection error, as well as computation time for projection and unprojection functions and their Jacobians.
Occupancy Networks: Learning 3D Reconstruction in Function Space
LMescheder/Occupancy-Networks
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CVPR 2019
With the advent of deep neural networks, learning-based approaches for 3D reconstruction have gained popularity.
Convolutional Occupancy Networks
autonomousvision/convolutional_occupancy_networks
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ECCV 2020
Recently, implicit neural representations have gained popularity for learning-based 3D reconstruction.
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.
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.
PCRNet: Point Cloud Registration Network using PointNet Encoding
PointNet has recently emerged as a popular representation for unstructured point cloud data, allowing application of deep learning to tasks such as object detection, segmentation and shape completion.
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.
