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Found 11 papers, 3 papers with code
A Tiny Machine Learning Model for Point Cloud Object Classification
On the other hand, the model size and inference complexity of DGCNN are 42X and 1203X of those of Green-PointHop, respectively.
PointFlowHop: Green and Interpretable Scene Flow Estimation from Consecutive Point Clouds
An efficient 3D scene flow estimation method called PointFlowHop is proposed in this work.
S3I-PointHop: SO(3)-Invariant PointHop for 3D Point Cloud Classification
Many point cloud classification methods are developed under the assumption that all point clouds in the dataset are well aligned with the canonical axes so that the 3D Cartesian point coordinates can be employed to learn features.
PCRP: Unsupervised Point Cloud Object Retrieval and Pose Estimation
An unsupervised point cloud object retrieval and pose estimation method, called PCRP, is proposed in this work.
GreenPCO: An Unsupervised Lightweight Point Cloud Odometry Method
GreenPCO is an unsupervised learning method that predicts object motion by matching features of consecutive point cloud scans.
GSIP: Green Semantic Segmentation of Large-Scale Indoor Point Clouds
It is named GSIP (Green Segmentation of Indoor Point clouds) and its performance is evaluated on a representative large-scale benchmark -- the Stanford 3D Indoor Segmentation (S3DIS) dataset.
R-PointHop: A Green, Accurate, and Unsupervised Point Cloud Registration Method
Inspired by the recent PointHop classification method, an unsupervised 3D point cloud registration method, called R-PointHop, is proposed in this work.
Unsupervised Point Cloud Registration via Salient Points Analysis (SPA)
An unsupervised point cloud registration method, called salient points analysis (SPA), is proposed in this work.
Unsupervised Feedforward Feature (UFF) Learning for Point Cloud Classification and Segmentation
The UFF method exploits statistical correlations of points in a point cloud set to learn shape and point features in a one-pass feedforward manner through a cascaded encoder-decoder architecture.
PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification
The PointHop method was recently proposed by Zhang et al. for 3D point cloud classification with unsupervised feature extraction.
PointHop: An Explainable Machine Learning Method for Point Cloud Classification
In the attribute building stage, we address the problem of unordered point cloud data using a space partitioning procedure and developing a robust descriptor that characterizes the relationship between a point and its one-hop neighbor in a PointHop unit.
