Fast LiDAR Upsampling using Conditional Diffusion Models

Sander Elias Magnussen Helgesen, Kazuto Nakashima, Jim Tørresen, Ryo Kurazume

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The search for refining 3D LiDAR data has attracted growing interest motivated by recent techniques such as supervised learning or generative model-based methods. Existing approaches have shown the possibilities for using diffusion models to generate refined LiDAR data with high fidelity, although the performance and speed of such methods have been limited. These limitations make it difficult to execute in real-time, causing the approaches to struggle in real-world tasks such as autonomous navigation and human-robot interaction. In this work, we introduce a novel approach based on conditional diffusion models for fast and high-quality sparse-to-dense upsampling of 3D scene point clouds through an image representation. Our method employs denoising diffusion probabilistic models trained with conditional inpainting masks, which have been shown to give high performance on image completion tasks. We introduce a series of experiments, including multiple datasets, sampling steps, and conditional masks. This paper illustrates that our method outperforms the baselines in sampling speed and quality on upsampling tasks using the KITTI-360 dataset. Furthermore, we illustrate the generalization ability of our approach by simultaneously training on real-world and synthetic datasets, introducing variance in quality and environments.

Original languageEnglish
Title of host publication33rd IEEE International Conference on Robot and Human Interactive Communication, ROMAN 2024
PublisherIEEE Computer Society
Pages272-277
Number of pages6
ISBN (Electronic)9798350375022
DOIs
Publication statusPublished - Aug 2024
Event33rd IEEE International Conference on Robot and Human Interactive Communication, ROMAN 2024 - Pasadena, United States
Duration: Aug 26 2024Aug 30 2024

Publication series

NameIEEE International Workshop on Robot and Human Communication, RO-MAN
ISSN (Print)1944-9445
ISSN (Electronic)1944-9437

Conference

Conference33rd IEEE International Conference on Robot and Human Interactive Communication, ROMAN 2024
Country/TerritoryUnited States
CityPasadena
Period8/26/248/30/24

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Software

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