Graph Attention-Driven Bayesian Deep Unrolling for Dual-Peak Single-Photon Lidar Imaging
Kyungmin Choi, JaKeoung Koo, Stephen McLaughlin, Abderrahim Halimi·April 03, 2025
Summary
A Graph Attention-Driven Bayesian Deep Unrolling method for dual-peak single-photon Lidar imaging is introduced, addressing noisy environments with multiple targets per pixel. Combining statistical methods and deep learning, it offers accuracy and uncertainty quantification, using a hierarchical Bayesian model, dual depth maps, and geometric deep learning for feature extraction. Competitive performance is demonstrated on synthetic and real data, outperforming existing methods.
Introduction
Background
Overview of single-photon Lidar imaging
Challenges in noisy environments with multiple targets per pixel
Objective
Introduce a novel method for improving accuracy and uncertainty quantification in dual-peak single-photon Lidar imaging
Method
Data Collection
Description of Lidar data acquisition process
Data Preprocessing
Techniques for handling noisy data and preparing for analysis
Graph Attention Mechanism
Explanation of how graph attention is applied to Lidar data
Bayesian Deep Unrolling
Integration of Bayesian inference with deep learning for iterative refinement
Hierarchical Bayesian Model
Description of the hierarchical structure and its role in modeling uncertainty
Dual Depth Maps
Explanation of the dual-depth map approach for improved target separation
Geometric Deep Learning
Utilization of geometric deep learning for feature extraction from Lidar data
Algorithmic Steps
Detailed outline of the method's implementation process
Results
Synthetic Data Analysis
Demonstration of method performance on simulated data
Real Data Application
Case studies showcasing the method's effectiveness on actual Lidar data
Comparison with Existing Methods
Quantitative and qualitative comparison to current state-of-the-art techniques
Discussion
Advantages and Limitations
Analysis of the method's strengths and potential areas for improvement
Future Work
Suggestions for further research and development
Conclusion
Summary of contributions and implications for Lidar imaging technology
Basic info
papers
computer vision and pattern recognition
machine learning
artificial intelligence
Advanced features
Insights
What are the key components of the hierarchical Bayesian model used in this method?
What role does geometric deep learning play in feature extraction for this Lidar imaging method?
How does the Graph Attention-Driven Bayesian Deep Unrolling method integrate statistical methods with deep learning for Lidar imaging?
In what ways does the method ensure accuracy and uncertainty quantification in noisy environments?