3D Shape Classification
29 papers with code • 1 benchmarks • 1 datasets
Image: Sun et al
Benchmarks
These leaderboards are used to track progress in 3D Shape Classification
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
Libraries
Use these libraries to find 3D Shape Classification models and implementations
Pix3D
Most implemented papers
PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition
code-implementation1/Code9
•
•
With the recent proliferation of deep learning, various deep models with different representations have achieved the state-of-the-art performance.
Cross-Domain 3D Equivariant Image Embeddings
machc/spherical_embeddings
•
•
This embedding encodes images with 3D shape properties and is equivariant to 3D rotations of the observed object.
Equivariant Multi-View Networks
daniilidis-group/emvn
•
•
ICCV 2019
Several popular approaches to 3D vision tasks process multiple views of the input independently with deep neural networks pre-trained on natural images, achieving view permutation invariance through a single round of pooling over all views.
A Topological Nomenclature for 3D Shape Analysis in Connectomics
Next, we develop nomenclature rules for pyramidal neurons and mitochondria from the reduced graph and finally learn the feature embedding for shape manipulation.
Fine-Grained 3D Shape Classification with Hierarchical Part-View Attentions
According to our experiments under this fine-grained dataset, we find that state-of-the-art methods are significantly limited by the small variance among subcategories in the same category.
View-GCN: View-Based Graph Convolutional Network for 3D Shape Analysis
weixmath/view-GCN
•
•
CVPR 2020
View-based approach that recognizes 3D shape through its projected 2D images has achieved state-of-the-art results for 3D shape recognition.
Unsupervised Deep Shape Descriptor With Point Distribution Learning
WordBearerYI/Unsupervised-Deep-Shape-Descriptor-with-Point-Distribution-Learning
•
•
CVPR 2020
This paper proposes a novel probabilistic framework for the learning of unsupervised deep shape descriptors with point distribution learning.
Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds
To date, various 3D scene understanding tasks still lack practical and generalizable pre-trained models, primarily due to the intricate nature of 3D scene understanding tasks and their immense variations introduced by camera views, lighting, occlusions, etc.
POINTVIEW-GCN: 3D SHAPE CLASSIFICATION WITH MULTI-VIEW POINT CLOUDS
SMohammadi89/PointView-GCN
•
•
IEEE International Conference on Image Processing 2021
We address 3D shape classification with partial point cloud inputs captured from multiple viewpoints around the object.
TreeGCN-ED: Encoding Point Cloud using a Tree-Structured Graph Network
Point cloud is one of the widely used techniques for representing and storing 3D geometric data.
