3D Shape Classification

20 papers with code • 1 benchmarks • 2 datasets

Image: Sun et al

Greatest papers with code

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.

3D Object Detection 3D Shape Classification +2

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.

3D Shape Classification General Classification +3

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.

3D Shape Classification 3D Shape Representation +1

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.

3D Shape Classification Autonomous Driving

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.

3D Shape Classification 3D Shape Retrieval +1

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.

3D Shape Classification 3D Shape Recognition

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.

3D Object Recognition 3D Object Retrieval +4

Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds

yichen928/STRL ICCV 2021

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.

3D Object Detection 3D Semantic Segmentation +4

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.

3D Classification 3D Object Retrieval +5