3D Shape Reconstruction
Leaderboards
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
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Greatest papers with code
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
• facebookresearch/SparseConvNet
• 
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database.
Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling
CVPR 2018
• xingyuansun/pix3d
• 
We study 3D shape modeling from a single image and make contributions to it in three aspects.
SOTA for 3D Shape Retrieval on Pix3D
3D RECONSTRUCTION 3D SHAPE MODELING 3D SHAPE RECONSTRUCTION MULTI-TASK LEARNING POSE ESTIMATION VIEWPOINT ESTIMATION
SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator
Intrinsic graph convolution operators with differentiable kernel functions play a crucial role in analyzing 3D shape meshes.
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.
3D OBJECT RECONSTRUCTION 3D RECONSTRUCTION 3D SHAPE RECONSTRUCTION
Multiview Aggregation for Learning Category-Specific Shape Reconstruction
NeurIPS 2019
• drsrinathsridhar/xnocs
•
We investigate the problem of learning category-specific 3D shape reconstruction from a variable number of RGB views of previously unobserved object instances.
PAI-GCN: Permutable Anisotropic Graph Convolutional Networks for 3D Shape Representation Learning
The recent success of convolutional neural networks (CNNs) for image analysis suggests the value of adapting insight from CNN to 3D shapes.
3D SHAPE RECONSTRUCTION 3D SHAPE REPRESENTATION REPRESENTATION LEARNING
Multi-modal 3D Shape Reconstruction Under Calibration Uncertainty using Parametric Level Set Methods
This method not only allows us to analytically and compactly represent the object, it also confers on us the ability to overcome calibration related noise that originates from inaccurate acquisition parameters.
Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers
CVPR 2018
• JeremyFisher/deep_level_sets
•
We scale this baseline to higher resolutions by proposing a memory-efficient shape encoding, which recursively decomposes a 3D shape into nested shape layers, similar to the pieces of a Matryoshka doll.
Domain-Adaptive Single-View 3D Reconstruction
ICCV 2019
• Gitikameher/Domain-Adaptive-Single-View-3D-Reconstruction
•
In this paper, we propose a framework to improve over these challenges using adversarial training.
3D RECONSTRUCTION 3D SHAPE RECONSTRUCTION DOMAIN ADAPTATION SINGLE-VIEW 3D RECONSTRUCTION
Domain-Adaptive Single-View 3D Reconstruction
ICCV 2019
• Gitikameher/Domain-Adaptive-Single-View-3D-Reconstruction
•
In this paper, we propose a framework to improve over these challenges using adversarial training.
3D RECONSTRUCTION 3D SHAPE RECONSTRUCTION DOMAIN ADAPTATION SINGLE-VIEW 3D RECONSTRUCTION