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Deep Learning for 3D Point Clouds: A Survey

27 Dec 2019QingyongHu/SoTA-Point-Cloud

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 AUTONOMOUS DRIVING

Learning Equivariant Representations

4 Dec 2020daniilidis-group/spherical-cnn

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

3D SHAPE CLASSIFICATION IMAGE CLASSIFICATION POSE ESTIMATION VIDEO RECOGNITION

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 CLASSIFICATION 3D SHAPE REPRESENTATION

Generating 3D Adversarial Point Clouds

CVPR 2019 xiangchong1/3d-adv-pc

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

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

ICLR 2019 maxjiang93/DDSL

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

3D SHAPE CLASSIFICATION 4

Equivariant Multi-View Networks

ICCV 2019 daniilidis-group/emvn

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 SCENE CLASSIFICATION

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 CLASSIFICATION 3D SHAPE RECOGNITION

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

CVPR 2018 popcornell/keras-triplet-center-loss

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 3D SHAPE CLASSIFICATION METRIC LEARNING