Fast LiDAR Upsampling using Conditional Diffusion Models

• URL: http://arxiv.org/abs/2405.04889v2
• Date: Tue, 23 Jul 2024 06:51:06 GMT
• Title: Fast LiDAR Upsampling using Conditional Diffusion Models
• Authors: Sander Elias Magnussen Helgesen, Kazuto Nakashima, Jim Tørresen, Ryo Kurazume,
• Abstract summary: Existing approaches have shown the possibilities for using diffusion models to generate refined LiDAR data with high fidelity. We introduce a novel approach based on conditional diffusion models for fast and high-quality sparse-to-dense upsampling of 3D scene point clouds. Our method employs denoising diffusion probabilistic models trained with conditional inpainting masks, which have been shown to give high performance on image completion tasks.
• Score: 1.3709133749179265
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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