mlsanityData to 14 Oct 2026

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

arXiv 2509.18917 ↗First posted 23 Sep 2025cs.CV cs.AIcited by 0

On the map: Diffusion models → 3D, point cloud and scene generation

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models. Builds on 13 of its 21 references that are in the map. First posted on arXiv 23 Sep 2025.

Builds on

13 of 21 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Towards Realistic Scene Generation with LiDAR Diffusion Models31 Mar

Other references · 12

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  5. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
  6. 2017CARLA: An Open Urban Driving Simulatorcited by 2,449
  7. 2021KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3Dcited by 575
  8. 2022Scalable Adaptive Computation for Iterative Generationcited by 195
  9. 2022Learning to Generate Realistic LiDAR Point Cloudscited by 93
  10. 2023UltraLiDAR: Learning Compact Representations for LiDAR Completion and Generationcited by 78
  11. 2018Deep Generative Modeling of LiDAR Datacited by 71
  12. 2024Interaction of Autonomous and Manually Controlled Vehicles Multiscenario Vehicle Interaction Datasetcited by 5

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  1. 2023LiDAR Data Synthesis with Denoising Diffusion Probabilistic Models
  2. 2024Scaling Diffusion Models to Real-World 3D LiDAR Scene Completion
  3. 2024DiffSSC: Semantic LiDAR Scan Completion using Denoising Diffusion Probabilistic Models
  4. 2024Fast LiDAR Upsampling using Conditional Diffusion Models
  5. 2024Taming Transformers for Realistic Lidar Point Cloud Generation
  6. 2024Test-Time Adaptation of 3D Point Clouds via Denoising Diffusion Models
  7. 2026CloudDiffusion: Diffusion-Based Scene Completion in the Point Cloud Domain
  8. 2025LiNeXt: Revisiting LiDAR Completion with Efficient Non-Diffusion Architectures