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, 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.
Abstract (translated)
寻找精化3D LiDAR数据的搜索吸引了越来越多的关注,这是由最近使用的如监督学习或基于生成模型的方法等技术引起的。虽然已经证明了使用扩散模型生成具有高保真度的精化LiDAR数据的可能性,但这种方法的性能和速度仍然有限。这些限制使得在实时执行中很难实现,导致在现实世界的任务(如自主导航和人类机器人交互)中,这些方法遇到困难。 在本文中,我们介绍了一种基于条件扩散模型的新的方法,用于通过图像表示对3D场景点云进行高保真度的平滑和压缩。我们的方法采用带条件修补掩码的噪声扩散概率模型进行训练,这些模型已经在图像完成任务中表现出良好的性能。我们介绍了一系列实验,包括多个数据集、采样步骤和条件掩码,以确定理想的配置,在性能和推理速度之间取得平衡。本文证明了,我们的方法在KITTI-360数据集上的采样速度和质量方面超过了基线。此外,我们还通过同时在一手真实世界和合成数据上训练,展示了我们方法的一般化能力。我们还展示了在不同质量和环境下的平滑和压缩效果。
URL
https://arxiv.org/abs/2405.04889
