3D Shape Recognition
13 papers with code • 0 benchmarks • 1 datasets
Image: Wei et al
Benchmarks
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Libraries
Use these libraries to find 3D Shape Recognition models and implementations
Hypersim
Latest papers
MVTN: Learning Multi-View Transformations for 3D Understanding
ajhamdi/mvtorch
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Multi-view projection techniques have shown themselves to be highly effective in achieving top-performing results in the recognition of 3D shapes.
PointMCD: Boosting Deep Point Cloud Encoders via Multi-view Cross-modal Distillation for 3D Shape Recognition
In this paper, we explore the possibility of boosting deep 3D point cloud encoders by transferring visual knowledge extracted from deep 2D image encoders under a standard teacher-student distillation workflow.
Points to Patches: Enabling the Use of Self-Attention for 3D Shape Recognition
While the Transformer architecture has become ubiquitous in the machine learning field, its adaptation to 3D shape recognition is non-trivial.
NeuralTailor: Reconstructing Sewing Pattern Structures from 3D Point Clouds of Garments
maria-korosteleva/Garment-Pattern-Estimation
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The fields of SocialVR, performance capture, and virtual try-on are often faced with a need to faithfully reproduce real garments in the virtual world.
PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representation
myavartanoo/PolyNet_PyTorch
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3D shape representation and its processing have substantial effects on 3D shape recognition.
Learning Canonical View Representation for 3D Shape Recognition with Arbitrary Views
In this way, each 3D shape with arbitrary views is represented by a fixed number of canonical view features, which are further aggregated to generate a rich and robust 3D shape representation for shape recognition.
MVTN: Multi-View Transformation Network for 3D Shape Recognition
MVTN exhibits clear performance gains in the tasks of 3D shape classification and 3D shape retrieval without the need for extra training supervision.
View-GCN: View-Based Graph Convolutional Network for 3D Shape Analysis
weixmath/view-GCN
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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.
On Learning Sets of Symmetric Elements
Haggaim/On-Learning-Sets-of-Symmetric-Elements
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ICML 2020
We first characterize the space of linear layers that are equivariant both to element reordering and to the inherent symmetries of elements, like translation in the case of images.
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
lucidrains/perceiver-pytorch
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Many machine learning tasks such as multiple instance learning, 3D shape recognition, and few-shot image classification are defined on sets of instances.
