Stars
DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing (ICCV 2019)
Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://intellabs.github.io/distiller
MANet: Multimodal Attention Network based Point- View fusion for 3D Shape Recognition
Experiments on point cloud segmentation.
Code for PointAugment: an Auto-Augmentation Framework for Point Cloud Classification, CVPR 2020 (Oral)
The AAAI-2020 Paper(Oral):"TANet: Robust 3D Object Detection from Point Clouds with Triple Attention"
A list of papers and datasets about point cloud analysis (processing)
🐨 (pytorch version) TOMM2017 A Discriminatively Learned CNN Embedding for Person Re-identification 🐨
Pytorch Implementationg of “Learning Efficient Convolutional Networks through Network Slimming”
Linked Dynamic Graph CNN: Learning through Point Cloud by Linking Hierarchical Features
A project to implement some state-of-the-art 3D network architecture.
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters
PU-GAN: a Point Cloud Upsampling Adversarial Network, ICCV, 2019
pytorch implementation of >>Patch-base progressive 3D Point Set Upsampling<<
Experience of The Second Time Competition
Zhangsihao-Yang / PU-Net
Forked from yulequan/PU-NetPU-Net: Point Cloud Upsampling Network, CVPR, 2018 (https://arxiv.org/abs/1801.06761)
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space