3D Shape Classification

29 papers with code • 1 benchmarks • 1 datasets

Image: Sun et al


Use these libraries to find 3D Shape Classification models and implementations
2 papers


Most implemented papers

PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition

2023-MindSpore-4/Code10 23 Aug 2018

With the recent proliferation of deep learning, various deep models with different representations have achieved the state-of-the-art performance.

Deep Learning for 3D Point Clouds: A Survey

QingyongHu/SoTA-Point-Cloud 27 Dec 2019

To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds.

Generating 3D Adversarial Point Clouds

xiangchong1/3d-adv-pc CVPR 2019

Deep neural networks are known to be vulnerable to adversarial examples which are carefully crafted instances to cause the models to make wrong predictions.

MeshNet: Mesh Neural Network for 3D Shape Representation

iMoonLab/MeshNet 28 Nov 2018

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

Convolutional Neural Networks on non-uniform geometrical signals using Euclidean spectral transformation

maxjiang93/DDSL ICLR 2019

It has been challenging to analyze signals with mixed topologies (for example, point cloud with surface mesh).

MVTN: Multi-View Transformation Network for 3D Shape Recognition

ajhamdi/MVTN ICCV 2021

MVTN exhibits clear performance gains in the tasks of 3D shape classification and 3D shape retrieval without the need for extra training supervision.

Learning Equivariant Representations

daniilidis-group/spherical-cnn 4 Dec 2020

In this thesis, we extend equivariance to other kinds of transformations, such as rotation and scaling.

Triplet-Center Loss for Multi-View 3D Object Retrieval

popcornell/keras-triplet-center-loss CVPR 2018

Most existing 3D object recognition algorithms focus on leveraging the strong discriminative power of deep learning models with softmax loss for the classification of 3D data, while learning discriminative features with deep metric learning for 3D object retrieval is more or less neglected.