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3D Shape Analysis

8 papers with code · Computer Vision
Subtask of 3D

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MeshCNN: A Network with an Edge

16 Sep 2018ranahanocka/MeshCNN

In this paper, we utilize the unique properties of the mesh for a direct analysis of 3D shapes using MeshCNN, a convolutional neural network designed specifically for triangular meshes.

3D PART SEGMENTATION 3D SHAPE ANALYSIS CUBE ENGRAVING CLASSIFICATION

Adaptive O-CNN: A Patch-based Deep Representation of 3D Shapes

21 Sep 2018Microsoft/O-CNN

The Adaptive O-CNN encoder takes the planar patch normal and displacement as input and performs 3D convolutions only at the octants at each level, while the Adaptive O-CNN decoder infers the shape occupancy and subdivision status of octants at each level and estimates the best plane normal and displacement for each leaf octant.

3D SHAPE ANALYSIS

MeshNet: Mesh Neural Network for 3D Shape Representation

28 Nov 2018iMoonLab/MeshNet

However, there is little effort on using mesh data in recent years, due to the complexity and irregularity of mesh data.

3D SHAPE ANALYSIS 3D SHAPE CLASSIFICATION 3D SHAPE REPRESENTATION

FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis

CVPR 2018 nitika-verma/FeaStNet

Convolutional neural networks (CNNs) have massively impacted visual recognition in 2D images, and are now ubiquitous in state-of-the-art approaches.

3D SHAPE ANALYSIS

Attention-Based LSTM for Psychological Stress Detection from Spoken Language Using Distant Supervision

31 May 2018gentaiscool/lstm-attention

The bidirectional LSTM model with attention is found to be the best model in terms of accuracy (74. 1%) and f-score (74. 3%).

3D SHAPE ANALYSIS

View-GCN: View-Based Graph Convolutional Network for 3D Shape Analysis

CVPR 2020 weixmath/view-GCN

View-based approach that recognizes 3D shape through its projected 2D images has achieved state-of-the-art results for 3D shape recognition.

3D SHAPE ANALYSIS 3D SHAPE CLASSIFICATION 3D SHAPE RECOGNITION

Geometric deep learning on graphs and manifolds using mixture model CNNs

CVPR 2017 HeapHop30/graph-attention-nets

Recently, there has been an increasing interest in geometric deep learning, attempting to generalize deep learning methods to non-Euclidean structured data such as graphs and manifolds, with a variety of applications from the domains of network analysis, computational social science, or computer graphics.

3D SHAPE ANALYSIS DOCUMENT CLASSIFICATION OBJECT DETECTION SPEECH RECOGNITION

Perturbation Robust Representations of Topological Persistence Diagrams

ECCV 2018 anirudhsom/Perturbed-Topological-Signature

However, persistence diagrams are multi-sets of points and hence it is not straightforward to fuse them with features used for contemporary machine learning tools like deep-nets.

3D SHAPE ANALYSIS