103 citations · 199 across the 5 of their papers we have counts for
6 papers
GSIP: Green Semantic Segmentation of Large-Scale Indoor Point Clouds
Min Zhang, Pranav Kadam, Shan Liu +1
An efficient solution to semantic segmentation of large-scale indoor scene point clouds is proposed in this work. It is named GSIP (Green Segmentation of Indoor Point clouds) and i…
R-PointHop: A Green, Accurate, and Unsupervised Point Cloud Registration Method
Pranav Kadam, Min Zhang, Shan Liu +1
Inspired by the recent PointHop classification method, an unsupervised 3D point cloud registration method, called R-PointHop, is proposed in this work. R-PointHop first determines…
Unsupervised Point Cloud Registration via Salient Points Analysis (SPA)
Pranav Kadam, Min Zhang, Shan Liu +1
An unsupervised point cloud registration method, called salient points analysis (SPA), is proposed in this work. The proposed SPA method can register two point clouds effectively u…
Unsupervised Feedforward Feature (UFF) Learning for Point Cloud Classification and Segmentation
Min Zhang, Pranav Kadam, Shan Liu +1
In contrast to supervised backpropagation-based feature learning in deep neural networks (DNNs), an unsupervised feedforward feature (UFF) learning scheme for joint classification…
PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification
Min Zhang, Yifan Wang, Pranav Kadam +2
The PointHop method was recently proposed by Zhang et al. for 3D point cloud classification with unsupervised feature extraction. It has an extremely low training complexity while…
PointHop: An Explainable Machine Learning Method for Point Cloud Classification
Min Zhang, Haoxuan You, Pranav Kadam +2
An explainable machine learning method for point cloud classification, called the PointHop method, is proposed in this work. The PointHop method consists of two stages: 1) local-to…