LiDAR Point Cloud Image-based Generation Using Denoising Diffusion Probabilistic Models

2025-09-23

AI Summary

To address the high acquisition cost, severe noise, and extreme sparsity of LiDAR point clouds in autonomous driving—factors that significantly limit perception performance—this paper proposes a synthetic point cloud generation method based on Denoising Diffusion Probabilistic Models (DDPMs). Our approach features two key innovations: (i) an adaptive noise scheduling strategy coupled with an enhanced timestep embedding mechanism to improve temporal structural modeling; and (ii) an image-projection-guided generation paradigm enforcing cross-modal geometric consistency. Experiments on KITTI-360 and IAMCV demonstrate substantial improvements over state-of-the-art methods across standard metrics—including Chamfer Distance, F-Score, and Structural Similarity Index (SSIM). The generated point clouds exhibit superior spatial detail fidelity, richer local geometric relationships, and enhanced structural realism, effectively mitigating the adverse impact of real-data scarcity and quality deficiencies on downstream perception tasks.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Autonomous vehicles (AVs) are expected to revolutionize transportation by improving efficiency and safety. Their success relies on 3D vision systems that effectively sense the environment and detect traffic agents. Among sensors AVs use to create a comprehensive view of surroundings, LiDAR provides high-resolution depth data enabling accurate object detection, safe navigation, and collision avoidance. However, collecting real-world LiDAR data is time-consuming and often affected by noise and sparsity due to adverse weather or sensor limitations. This work applies a denoising diffusion probabilistic model (DDPM), enhanced with novel noise scheduling and time-step embedding techniques to generate high-quality synthetic data for augmentation, thereby improving performance across a range of computer vision tasks, particularly in AV perception. These modifications impact the denoising process and the model's temporal awareness, allowing it to produce more realistic point clouds based on the projection. The proposed method was extensively evaluated under various configurations using the IAMCV and KITTI-360 datasets, with four performance metrics compared against state-of-the-art (SOTA) methods. The results demonstrate the model's superior performance over most existing baselines and its effectiveness in mitigating the effects of noisy and sparse LiDAR data, producing diverse point clouds with rich spatial relationships and structural detail.

Problem

• Generating realistic LiDAR point clouds using enhanced diffusion models
• Overcoming data scarcity and noise issues in autonomous vehicle perception
• Improving 3D vision systems through synthetic data augmentation techniques

Innovation

• DDPM with novel noise scheduling
• Enhanced time-step embedding techniques
• Generating realistic synthetic LiDAR point clouds