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, to determine the ideal configuration, striking a balance between
performance and inference speed. 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.

本研究介绍了基于条件扩散模型的快速高质量稀疏到稠密三维场景点云的上采样方法，并利用 KITTI-360 数据集证明了该方法在采样速度和质量上优于基准方法。