A Bayesian Approach for 3D Guided Video Super-Resolution of Single-Photon Lidar Data

Abderrahim Halimi; Stephen McLaughlin

doi:10.1109/SSP64130.2025.11073472

A Bayesian Approach for 3D Guided Video Super-Resolution of Single-Photon Lidar Data

Abderrahim Halimi, Stephen McLaughlin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

This paper introduces a Bayesian algorithm for the robust reconstruction and super-resolution of 3D video single-photon LiDAR data. The focus is on challenging scenarios with low-resolution LiDAR data, sparse photon returns or high background noise as observed in real-world applications. The proposed hierarchical Bayesian approach leverages multiscale histogram information and a high-resolution reflectivity guidance to provide high-resolution depth estimates along with corresponding uncertainty measures, aiding in better decision-making. Correlations between variables are enforced through a weighted scheme, enabling the integration of guidance from other sensors or advanced algorithms. Results on synthetic data demonstrate improved scene reconstruction in extreme conditions compared to existing methods.

Original language	English
Title of host publication	2025 IEEE Statistical Signal Processing Workshop
Publisher	IEEE
Pages	16-20
Number of pages	5
ISBN (Electronic)	9798331518004
DOIs	https://doi.org/10.1109/SSP64130.2025.11073472
Publication status	Published - 16 Jul 2025
Event	2025 IEEE Statistical Signal Processing Workshop - Edinburgh, United Kingdom Duration: 8 Jun 2025 → 11 Jun 2025 https://2025.ieeessp.org/

Workshop

Workshop	2025 IEEE Statistical Signal Processing Workshop
Abbreviated title	SSP 2025
Country/Territory	United Kingdom
City	Edinburgh
Period	8/06/25 → 11/06/25
Internet address	https://2025.ieeessp.org/

Keywords

3D reconstruction
3D video super-resolution
Bayesian inference
Single-photon LiDAR

ASJC Scopus subject areas

Electrical and Electronic Engineering
Applied Mathematics
Signal Processing
Computer Science Applications

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10.1109/SSP64130.2025.11073472

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title = "A Bayesian Approach for 3D Guided Video Super-Resolution of Single-Photon Lidar Data",

abstract = "This paper introduces a Bayesian algorithm for the robust reconstruction and super-resolution of 3D video single-photon LiDAR data. The focus is on challenging scenarios with low-resolution LiDAR data, sparse photon returns or high background noise as observed in real-world applications. The proposed hierarchical Bayesian approach leverages multiscale histogram information and a high-resolution reflectivity guidance to provide high-resolution depth estimates along with corresponding uncertainty measures, aiding in better decision-making. Correlations between variables are enforced through a weighted scheme, enabling the integration of guidance from other sensors or advanced algorithms. Results on synthetic data demonstrate improved scene reconstruction in extreme conditions compared to existing methods.",

keywords = "3D reconstruction, 3D video super-resolution, Bayesian inference, Single-photon LiDAR",

author = "Abderrahim Halimi and Stephen McLaughlin",

year = "2025",

month = jul,

day = "16",

doi = "10.1109/SSP64130.2025.11073472",

language = "English",

pages = "16--20",

booktitle = "2025 IEEE Statistical Signal Processing Workshop",

publisher = "IEEE",

address = "United States",

note = "2025 IEEE Statistical Signal Processing Workshop, SSP 2025 ; Conference date: 08-06-2025 Through 11-06-2025",

url = "https://2025.ieeessp.org/",

}

TY - GEN

T1 - A Bayesian Approach for 3D Guided Video Super-Resolution of Single-Photon Lidar Data

AU - Halimi, Abderrahim

AU - McLaughlin, Stephen

PY - 2025/7/16

Y1 - 2025/7/16

N2 - This paper introduces a Bayesian algorithm for the robust reconstruction and super-resolution of 3D video single-photon LiDAR data. The focus is on challenging scenarios with low-resolution LiDAR data, sparse photon returns or high background noise as observed in real-world applications. The proposed hierarchical Bayesian approach leverages multiscale histogram information and a high-resolution reflectivity guidance to provide high-resolution depth estimates along with corresponding uncertainty measures, aiding in better decision-making. Correlations between variables are enforced through a weighted scheme, enabling the integration of guidance from other sensors or advanced algorithms. Results on synthetic data demonstrate improved scene reconstruction in extreme conditions compared to existing methods.

AB - This paper introduces a Bayesian algorithm for the robust reconstruction and super-resolution of 3D video single-photon LiDAR data. The focus is on challenging scenarios with low-resolution LiDAR data, sparse photon returns or high background noise as observed in real-world applications. The proposed hierarchical Bayesian approach leverages multiscale histogram information and a high-resolution reflectivity guidance to provide high-resolution depth estimates along with corresponding uncertainty measures, aiding in better decision-making. Correlations between variables are enforced through a weighted scheme, enabling the integration of guidance from other sensors or advanced algorithms. Results on synthetic data demonstrate improved scene reconstruction in extreme conditions compared to existing methods.

KW - 3D reconstruction

KW - 3D video super-resolution

KW - Bayesian inference

KW - Single-photon LiDAR

UR - https://www.scopus.com/pages/publications/105012191869

U2 - 10.1109/SSP64130.2025.11073472

DO - 10.1109/SSP64130.2025.11073472

M3 - Conference contribution

SP - 16

EP - 20

BT - 2025 IEEE Statistical Signal Processing Workshop

PB - IEEE

T2 - 2025 IEEE Statistical Signal Processing Workshop

Y2 - 8 June 2025 through 11 June 2025

ER -

A Bayesian Approach for 3D Guided Video Super-Resolution of Single-Photon Lidar Data

Abstract

Workshop

Keywords

ASJC Scopus subject areas

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