Prediction of vegetation coverage maps from High Density Lidar data, in a weakly supervised deep learning setting.
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Updated
Mar 15, 2022 - Python
Prediction of vegetation coverage maps from High Density Lidar data, in a weakly supervised deep learning setting.
Code for my masters thesis, "Deep Learning for Detecting Trees in the Urban Environment from Lidar"
Frustum Pointnet Implementation on KITTI and Lyft Dataset
Official implementation of the paper "Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space"
✨ PointNet++ feature extractor and output heads implemented in TensorFlow 1.15 with Keras Models
Applying RandAugment on PointNet++
A pytorch implementation of PointNet and PointNet++
Code and Data for the paper "LPF-Defense: 3D Adversarial Defense based on Frequency Analysis", PLoS ONE
Semantic segmentation of LIDAR point clouds from the KITTI-360 dataset using a modified PointNet2. This is a Python and PyTorch based implementation using Jupyter Notebooks.
Efficient Point Cloud Upsampling and Normal Estimation using Deep Learning for Robust Surface Reconstruction
PAPC is a deep learning for point clouds platform based on pure PaddlePaddle
A clean PointNet++ segmentation model implementation. Support batch of samples with different number of points.
This is the official pytorch implementation for paper: IF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration
[CVPR 2020, Oral] Category-Level Articulated Object Pose Estimation
A pointnet++ fork, with focus on semantic segmentation of differents datasets
Official Code for ICML 2021 paper "Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline"
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