3D Reconstruction
551 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
3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks
The decoder converts this representation into depth and normal maps capturing the underlying surface from several output viewpoints.
DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction
laughtervv/DISN
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NeurIPS 2019
Reconstructing 3D shapes from single-view images has been a long-standing research problem.
PU-GAN: a Point Cloud Upsampling Adversarial Network
Point clouds acquired from range scans are often sparse, noisy, and non-uniform.
BSP-Net: Generating Compact Meshes via Binary Space Partitioning
czq142857/BSP-NET-original
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CVPR 2020
The network is trained to reconstruct a shape using a set of convexes obtained from a BSP-tree built on a set of planes.
PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes
ChrisWu1997/PQ-NET
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CVPR 2020
We introduce PQ-NET, a deep neural network which represents and generates 3D shapes via sequential part assembly.
3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans
MIT-SPARK/Kimera
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Our second contribution is to provide the first fully automatic Spatial PerceptIon eNgine(SPIN) to build a DSG from visual-inertial data.
Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance
lioryariv/idr
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NeurIPS 2020
In this work we address the challenging problem of multiview 3D surface reconstruction.
Learning Implicit Surface Light Fields
In this work, we propose a novel implicit representation for capturing the visual appearance of an object in terms of its surface light field.
NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video
zju3dv/NeuralRecon
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CVPR 2021
We present a novel framework named NeuralRecon for real-time 3D scene reconstruction from a monocular video.
CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimation
This paper studies the complex task of simultaneous multi-object 3D reconstruction, 6D pose and size estimation from a single-view RGB-D observation.
