Single-View 3D Reconstruction
42 papers with code • 7 benchmarks • 13 datasets
Benchmarks
These leaderboards are used to track progress in Single-View 3D Reconstruction
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Datasets
CUB-200-2011
ShapeNet
ShapeNetCore
ABC Dataset
Hypersim
ABO
Common Objects in 3D
SceneNet
Parcel2D Real
ShapenetRender
Most implemented papers
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.
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.
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.
Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning
ShichenLiu/SoftRas
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ICCV 2019
Rendering bridges the gap between 2D vision and 3D scenes by simulating the physical process of image formation.
Learning to Detect 3D Reflection Symmetry for Single-View Reconstruction
In this work, we focus on object-level 3D reconstruction and present a geometry-based end-to-end deep learning framework that first detects the mirror plane of reflection symmetry that commonly exists in man-made objects and then predicts depth maps by finding the intra-image pixel-wise correspondence of the symmetry.
3D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single Image
3D reconstruction from single view images is an ill-posed problem.
Learning Single-View 3D Reconstruction with Limited Pose Supervision
stevenygd/3d-recon
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ECCV 2018
It is expensive to label images with 3D structure or precise camera pose.
Domain-Adaptive Single-View 3D Reconstruction
Gitikameher/Domain-Adaptive-Single-View-3D-Reconstruction
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ICCV 2019
In this paper, we propose a framework to improve over these challenges using adversarial training.
Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer
nv-tlabs/DIB-R
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NeurIPS 2019
Many machine learning models operate on images, but ignore the fact that images are 2D projections formed by 3D geometry interacting with light, in a process called rendering.
Hyperparameter-Free Losses for Model-Based Monocular Reconstruction
hyperparams-free/hyperparams-free-3D-losses
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This work proposes novel hyperparameter-free losses for single view 3D reconstruction with morphable models (3DMM).
